Pith. sign in

REVIEW 2 major objections 4 minor 208 references

Trust and Reputation in Data Sharing: A Survey

T0 review · 2 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Existing trust-and-reputation systems for data sharing evaluate the people, not the data.

desk verdict Useful data-sharing survey with a solid taxonomy, but the central 'no dedicated approaches' claim is internally contradicted by the authors' own classification of [188]. read the letter →

arxiv 2508.14028 v1 pith:M7YQETCL submitted 2025-08-19 cs.SI cs.CYcs.IR

Wenbo Wu , George Konstantinidis This is my paper
classification cs.SIcs.CYcs.IR
keywords trustandreputationmanagementsystemsdatasharingqualityassessmententity-centricvsdata-centricevaluationbidirectionalprivacy-preservingTRMStaxonomymetrics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey argues that Trust and Reputation Management Systems (TRMSs) built for domains like vehicular networks, IoT, multi-agent systems, and peer-to-peer networks are overwhelmingly entity-centric: they score how well a provider or device behaves, but rarely assess the quality of the data being shared or whether the data consumer complies with the agreed terms of use. The authors try to establish that this one-sided focus is a systematic gap across the entire literature, not just a limitation of a few systems. To make the gap visible, they contribute a four-layer TRMS architecture for data-sharing ecosystems, two new taxonomies for system design and trust evaluation, and a unified set of data-centric and entity-centric evaluation metrics. If the paper is right, future TRMSs should treat the shared asset's quality and the consumer's compliance as first-class trust signals, enabling genuinely bidirectional accountability in data markets and AI supply chains.

What carries the argument

The analytical engine of the survey is a pair of novel taxonomies applied to a purpose-built four-layer TRMS architecture: Layer 1 is the data-sharing ecosystem, Layer 2 captures atomic trust signals (explicit user feedback and implicit system monitoring of data quality, compliance, SLA, and security), Layer 3 infers reputation via aggregation strategies, pattern detection, and computational models, and Layer 4 exposes reputation queries, explainability, and dispute-resolution services. The System Design Taxonomy classifies TRMSs by architecture (centralized, decentralized, federated), granularity and adaptability, directionality (unidirectional vs. bidirectional), and privacy-preserving tec

What would settle it

A concrete way to test the gap claim: if a literature search in data-sharing contexts—data marketplaces, federated learning, health data exchange—surfaces a substantial number of TRMSs that already compute data-quality scores for the shared asset and also evaluate consumer compliance with bilateral ratings, then the claimed 'consistent gap' loses its force. A second check would be to have independent reviewers re-classify the same 23 systems using the paper's taxonomy; if their classifications differ materially on the data-centric and directionality columns, the gap assessment would not be sta

Watch

Extended reading notes

Core claim

The paper's central claim is that existing TRMSs are 'overwhelmingly entity-centric', overlooking 'a rigorous, data-centric assessment of the shared asset's quality and the bidirectional evaluation of the data consumer's compliance.' Through a survey of 23 systems across distributed autonomous systems (VANETs, IoT, MASs, P2P) and digital service ecosystems (healthcare, fog/edge, crowdsourcing, social networks, data markets), the authors show that nearly all reviewed systems evaluate provider behavior with unidirectional trust flows, while data-quality dimensions such as completeness, consistency, and timeliness are rarely first-class metrics, and consumer compliance with data-sharing agreeme

Load-bearing premise

The survey's conclusions rest on the assumption that the 23 selected papers fairly represent the broader TRMS literature and that the qualitative binary classifications in Tables IV and VIII accurately capture what each system can actually do.

Editorial extensions

If this is right

  • Data-sharing platforms would need TRMSs that score the dataset itself—authenticity, accuracy, completeness, timeliness, traceability—rather than relying on a provider's reputation as a proxy for data quality.
  • Bidirectional evaluation would let data providers rate consumer compliance with data-sharing agreements, closing the current accountability gap where only the provider is judged.
  • Unified benchmarks and context-aware weighting of trust signals, e.g., prioritizing privacy compliance in healthcare and latency in vehicular networks, become prerequisites for portable and comparable reputation scores.
  • LLM-based compliance monitoring, hedged by retrieval-augmented generation and human-in-the-loop verification, could turn regulatory adherence into a quantifiable trust metric.
  • Security-by-design principles, including threat modeling plus cryptographic tools such as zero-knowledge proofs and secure multi-party computation, would make the TRMS itself resilient to manipulation attacks like collusion and bad-mouthing.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The survey's gap claim implies that any TRMS that scores only entities is vulnerable to a 'trusted source, bad data' failure mode; a straightforward test would be comparing provider reputation scores against independently measured data-quality scores on the same dataset.
  • If the gap is real, then reputation portability between data platforms—raised in the paper only through one cited market study—is blocked not just by missing standards but by the absence of a shared data-quality measurement layer.
  • A practical extension the paper leaves implicit: a data-sharing TRMS could derive implicit data-quality signals automatically from schema checks, duplicate detection, freshness metadata, and provenance logs, which would be far harder to game than user ratings.
  • The proposed dual taxonomies could be reused as a checklist for grading any new TRMS design, which suggests an evaluative tool that the authors themselves do not explicitly build.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. The paper surveys trust and reputation management systems (TRMSs) from a data-sharing perspective. It defines trust, trustworthiness, and reputation; introduces a four-layer TRMS architecture for data sharing; proposes taxonomies for system design, trust evaluation, and data- and entity-centric metrics; and applies these frameworks to 23 TRMSs across seven domains (VANETs, IoT, MAS, P2P, healthcare, fog/edge, crowdsourcing, social networks, and data markets). The central claim is that existing TRMSs are overwhelmingly entity-centric and lack rigorous data-centric quality assessment and bidirectional evaluation of consumer compliance, and that no dedicated TRMS for data sharing has been proposed. The paper closes with open research directions, including explainability, compliance as a trust metric, and security by design.

Significance. If the central gap claim is stated accurately, the survey makes a useful contribution: it provides clear definitions (Definitions 2.1–2.8), a structured architecture (Fig. 1), two novel taxonomies (Figs. 2 and 4), and systematic comparison tables (Tables IV–XI) that make the field's coverage testable. The forward-looking sections on LLM-based compliance monitoring and explainable trust evaluation are concrete and useful. The paper does not claim machine-checked proofs or code, but its detailed tabular coding of 23 systems is a strength because it enables readers to inspect the evidence behind the claimed gap.

major comments (2)
  1. [Abstract; §IX Summary] The central claim is an existence claim and is contradicted by the paper's own evidence. The abstract states that 'there have not been dedicated approaches to data sharing,' and the §IX Summary states that existing TRMS evaluation frameworks are 'without exception, overwhelmingly entity-centric' and 'consistently overlook' data-centric quality and bidirectional consumer compliance. However, Table VIII codes [188] (Data Market, 2019) as supporting data-centric metrics (Integrity, Accuracy, Traceability) and entity-centric metrics including Compliance, Transparency, Accountability, Security, Consent, and Reputation; Table IX codes it as Bidirectional and Role-Specific; Table X codes it as Combined with Hybrid signals. The text in §IX.D calls [188] 'a purpose-built TRMS for a sensitive data market' and 'the only model identified that begins to bridge the entity-centric versus data-centric g
  2. [§IX, Tables IV–XI] The gap analysis rests on binary support marks whose coding methodology is not documented. No coding protocol, decision rules, or inter-rater validation is provided, and the absence marks are load-bearing for an absence claim. Internal inconsistencies suggest the coding is not yet reliable. For example, Table V lists N/A for [7]'s Evaluation Adaptability and Directionality, but Section IV defines those dimensions without an N/A option. In §IX.B the text says 'systems like [88], [92] support Data-Centric metrics like Authenticity, Integrity, and Validity,' but Table IV shows [92] supporting only Authenticity (and Reputation). Please add a coding appendix with per-paper justifications, correct the inconsistent entries, and re-check all table-derived summary statements against the tables.
minor comments (4)
  1. [Table VII] The header contains a typo: 'Pricacy Leakage' should be 'Privacy Leakage.'
  2. [§V opening] The sentence 'This section, therefore, we shift focus to trust evaluation' is ungrammatical; suggest 'This section therefore shifts focus to trust evaluation.'
  3. [§IX.D Data Market discussion] The phrase 'Its additional feature' should be 'An additional feature' or 'Its additional feature is...' for clarity.
  4. [Tables IV–XI] The tables use many symbols (●, ❍, ✓, ✗, N/A) without a single consolidated legend in each table; a short caption note or common legend would improve readability. Also, the spacing artifact 'V ANETs' appears throughout; this should be 'VANETs'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the survey's taxonomies and gap analysis rest on per-paper classifications, not on a self-citation chain; the absolute gap claim is internally overstated, but that is a correctness issue, not circularity.

full rationale

This is a survey with no equations, fitted parameters, or derived predictions, so most circularity modes do not apply. The central claim—that TRMSs are predominantly entity-centric and overlook data-centric quality and bidirectional consumer compliance—is supported by the paper's own binary classification tables (Tables IV, V, VI, VIII, IX, X), which are coded per system and are independent of the proposed architecture. Self-citations such as [25], [16], [163], and [31] appear as illustrative examples (e.g., 'frameworks like the one proposed by Konstantinidis et al. [25] enable customized consent within relational databases') and are not load-bearing for the gap conclusion. No uniqueness theorem or ansatz is imported from the authors' prior work, and no result is renamed as a prediction. One internal tension should be flagged as a correctness risk rather than circularity: Table VIII codes Chowdhury et al. [188] as a Combined, Bidirectional, purpose-built data-market TRMS with Compliance/Consent support, and Section IX calls it 'the only model identified that begins to bridge the entity-centric versus data-centric gap,' while the Section IX Summary says evaluation frameworks are 'without exception, overwhelmingly entity-centric' and 'consistently overlook' the two missing dimensions. That overstatement weakens the absolute form of the central claim, but it is a calibration/validity issue: the classification is not derived from the conclusion. The survey's gap analysis is self-contained in the sense that its classifications cite external papers and are falsifiable from those papers' contents.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claim depends on the representativeness of the sample, the validity of the taxonomy, and the correctness of the per-paper classifications. No free parameters or invented entities are introduced.

assumptions (3)
  • domain assumption The 23 reviewed papers are representative of the TRMS literature relevant to data sharing.
    The survey selects systems from VANETs, IoT, MASs, P2P, healthcare, fog/edge, crowdsourcing, social networks, and data markets, but does not justify completeness or representativeness.
  • domain assumption The proposed taxonomy dimensions (architecture, granularity, directionality, privacy, evaluation metrics) are comprehensive and orthogonal.
    The taxonomy is used to classify all systems; if a dimension is missing or overlapping, the resulting comparisons and gap findings could change.
  • domain assumption The binary classifications in Tables IV and VIII accurately reflect the capabilities of each cited system.
    The 'supported'/'not supported' marks are made by the authors based on their reading of the papers, without a formal rubric or inter-rater agreement.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Trust and Reputation in Data Sharing: A Survey." pith.science (2026). https://pith.science/paper/M7YQETCL

@misc{pith2026250814028,
  author       = {Pith},
  title        = {Pith review of: Trust and Reputation in Data Sharing: A Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M7YQETCL}},
  note         = {Machine review of arXiv:2508.14028}
}
read the original abstract

Data sharing is the fuel of the galloping artificial intelligence economy, providing diverse datasets for training robust models. Trust between data providers and data consumers is widely considered one of the most important factors for enabling data sharing initiatives. Concerns about data sensitivity, privacy breaches, and misuse contribute to reluctance in sharing data across various domains. In recent years, there has been a rise in technological and algorithmic solutions to measure, capture and manage trust, trustworthiness, and reputation in what we collectively refer to as Trust and Reputation Management Systems (TRMSs). Such approaches have been developed and applied to different domains of computer science, such as autonomous vehicles, or IoT networks, but there have not been dedicated approaches to data sharing and its unique characteristics. In this survey, we examine TRMSs from a data-sharing perspective, analyzing how they assess the trustworthiness of both data and entities across different environments. We develop novel taxonomies for system designs, trust evaluation framework, and evaluation metrics for both data and entity, and we systematically analyze the applicability of existing TRMSs in data sharing. Finally, we identify open challenges and propose future research directions to enhance the explainability, comprehensiveness, and accuracy of TRMSs in large-scale data-sharing ecosystems.

Figures

Figures reproduced from arXiv: 2508.14028 by the authors.

Figure 1
Figure 1. Vision Architecture of TRMS in Data Sharing [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. System Design Taxonomy for TRMSs • Service Level Agreement (SLA) Monitor: Tracks opera￾tional metrics like API uptime and latency. • Security Monitor: Logs security-relevant events, such as failed access attempts or potential data leakage. The output of this layer is a continuous stream of atomic trust records, each detailing a single, evidence-backed event. Layer 3: Trust Inference Mechanism (Reputation) This is th… view at source ↗
Figure 3
Figure 3. Centralized TRMS (left), Decentralized TRMS (middle), and Federated TRMS (right) [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Evaluation Framework of TRMSs Entity-centric models focus on evaluating the trustworthi￾ness of participating entities (e.g., individuals, devices, organi￾zations) by assessing their behavior, demonstrated properties, and relationships with others [115], [116]. These m…
Figure 5
Figure 5. Figure 5: Trust evaluation dimensions on data context of data quality, authenticity is used for validating the source, authorship, and origin of data, ensuring it has not been tampered with or misrepresented. It is closely linked to data integrity, which ensures that data remain…
Figure 6
Figure 6. Figure 6: Trust evaluation dimensions on entity accurate and nuanced understanding of the subject matter, supporting in-depth analysis and robust decision-making. The required level of precision depends on the specific needs of the decision-making process [151]; for example, hig…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

208 extracted references · 73 canonical work pages

  1. [188]

    Trust modeling for blockchain- based wearable data market,

    M. J. M. Chowdhury, M. S. Ferdous, K. Biswas, N. Chowdhury, A. Kayes, P. Watters, and A. Ng, “Trust modeling for blockchain- based wearable data market,” in 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) . IEEE, 2019, pp. 411–417

    work page 2019

  2. [7]

    A trust model for data sharing in smart cities,

    Q. H. Cao, I. Khan, R. Farahbakhsh, G. Madhusudan, G. M. Lee, and N. Crespi, “A trust model for data sharing in smart cities,” in2016 IEEE international conference on communications (ICC) . IEEE, 2016, pp. 1–7

    2016

  3. [88]

    Authenticpeer++: a trust management system for p2p networks,

    S. Alkharji, H. Kurdi, R. Altamimi, and E. Aloboud, “Authenticpeer++: a trust management system for p2p networks,” in 2017 European Modelling Symposium (EMS) . IEEE, 2017, pp. 191–196

    2017

  4. [92]

    Truetrust: a feedback-based trust management model without filtering feedbacks in p2p networks,

    X. Meng and G. Zhang, “Truetrust: a feedback-based trust management model without filtering feedbacks in p2p networks,” Peer-to-Peer Networking and Applications , vol. 13, pp. 175–189, 2020

    2020

  5. [1]

    From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets,

    J. R. Saura, D. Ribeiro-Soriano, and D. Palacios-Marqu ´es, “From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets,” International Journal of Information Management, vol. 60, p. 102331, 2021

    2021

  6. [2]

    A european strategy for data,

    C. European Commission, Directorate-General for Communica- tions Networks and Technology, “A european strategy for data,” 2020. [Online]. Available: https://eur-lex.europa.eu/legal- content/EN/TXT/?uri=CELEX%3A52020DC0066

    2020

  7. [3]

    Responsible practices for data sharing

    G. Alter and R. Gonzalez, “Responsible practices for data sharing.” American Psychologist, vol. 73, no. 2, p. 146, 2018

    2018

  8. [4]

    Privacy challenges and research opportunities for genomic data sharing,

    L. Bonomi, Y . Huang, and L. Ohno-Machado, “Privacy challenges and research opportunities for genomic data sharing,” Nature genetics, vol. 52, no. 7, pp. 646–654, 2020

    2020

Show all 208 references
  1. [5]

    National data strategy,

    Department for Science, Innovation and Technology and Department for Digital, Culture, Media & Sport, “National data strategy,”

  2. [6]

    A survey of trust and reputa- tion systems for online service provision,

    A. Jøsang, R. Ismail, and C. Boyd, “A survey of trust and reputa- tion systems for online service provision,” Decision support systems , vol. 43, no. 2, pp. 618–644, 2007

    2007

  3. [8]

    Machine learning based trust computational model for iot services,

    U. Jayasinghe, G. M. Lee, T.-W. Um, and Q. Shi, “Machine learning based trust computational model for iot services,” IEEE Transactions on Sustainable Computing , vol. 4, no. 1, pp. 39–52, 2018

    2018

  4. [9]

    Trust com- putational heuristic for social internet of things: A machine learning- based approach,

    S. Sagar, A. Mahmood, Q. Z. Sheng, and W. E. Zhang, “Trust com- putational heuristic for social internet of things: A machine learning- based approach,” in ICC 2020-2020 IEEE International Conference on Communications (ICC). IEEE, 2020, pp. 1–6

    2020

  5. [10]

    Enhancing trust management via blockchain in social internet of things,

    L. Wei, J. Wu, and C. Long, “Enhancing trust management via blockchain in social internet of things,” in 2020 Chinese Automation Congress (CAC). IEEE, 2020, pp. 159–164

    2020

  6. [11]

    A semi- centralized trust management model based on blockchain for data exchange in iot system,

    Y . Liu, C. Zhang, Y . Yan, X. Zhou, Z. Tian, and J. Zhang, “A semi- centralized trust management model based on blockchain for data exchange in iot system,” IEEE Transactions on Services Computing , vol. 16, no. 2, pp. 858–871, 2022

    2022

  7. [12]

    Dynamic trust management framework for robotic multi-agent sys- tems,

    I. Zikratov, O. Maslennikov, I. Lebedev, A. Ometov, and S. Andreev, “Dynamic trust management framework for robotic multi-agent sys- tems,” in Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 16th International Conference, NEW2AN 2016, and 9th Confer...

    2016

  8. [13]

    A trust management and misbehaviour detection mechanism for multi-agent systems and its application to intelligent transportation systems,

    J. M ¨uller, T. Meuser, R. Steinmetz, and M. Buchholz, “A trust management and misbehaviour detection mechanism for multi-agent systems and its application to intelligent transportation systems,” in 2019 IEEE 15th International Conference on Control and Automation (ICCA). IEEE...

    2019

  9. [14]

    A trust management system for multi-agent system in smart grids using blockchain technology,

    O. Samuel, N. Javaid, A. Khalid, M. Imrarn, and N. Nasser, “A trust management system for multi-agent system in smart grids using blockchain technology,” in GLOBECOM 2020-2020 IEEE Global Communications Conference. IEEE, 2020, pp. 1–6

    2020

  10. [15]

    A general trust framework for multi-agent systems,

    M. Cheng, C. Yin, J. Zhang, S. Nazarian, J. Deshmukh, and P. Bogdan, “A general trust framework for multi-agent systems,” in Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems , 2021, pp. 332–340. [Online]. Available: https://www.ifaam...

    2021

  11. [16]

    Secure and trustworthy cooperation framework for multi-vendor autonomous systems,

    E. W. Wu, M. Jurt, and Y . Jin, “Secure and trustworthy cooperation framework for multi-vendor autonomous systems,” TechRxiv preprint, 2024

    2024

  12. [17]

    Bars: A blockchain-based anonymous reputation system for trust management in vanets,

    Z. Lu, Q. Wang, G. Qu, and Z. Liu, “Bars: A blockchain-based anonymous reputation system for trust management in vanets,” in 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Sc...

    2018

  13. [18]

    Blockchain enabled trust- based location privacy protection scheme in vanet,

    B. Luo, X. Li, J. Weng, J. Guo, and J. Ma, “Blockchain enabled trust- based location privacy protection scheme in vanet,” IEEE Transactions on Vehicular Technology, vol. 69, no. 2, pp. 2034–2048, 2019

  14. [19]

    A scalable blockchain based trust management in vanet routing protocol,

    S. Kudva, S. Badsha, S. Sengupta, H. La, I. Khalil, and M. Atiquzza- man, “A scalable blockchain based trust management in vanet routing protocol,” Journal of Parallel and Distributed Computing, vol. 152, pp. 144–156, 2021

    2021

  15. [20]

    Blockchain- based distributed management system for trust in vanet,

    Y . Inedjaren, M. Maachaoui, B. Zeddini, and J.-P. Barbot, “Blockchain- based distributed management system for trust in vanet,” Vehicular Communications, vol. 30, p. 100350, 2021. [Online]. Available: https: //www.sciencedirect.com/science/article/pii/S221420962100019X

    2021

  16. [21]

    Behavior analysis and blockchain based trust management in vanets,

    H. Liu, D. Han, and D. Li, “Behavior analysis and blockchain based trust management in vanets,” Journal of Parallel and Distributed Computing, vol. 151, pp. 61–69, 2021

    2021

  17. [22]

    Data sharing fundamentals: characteristics and definition,

    I. Jussen, J. Schweihoff, V . Dahms, F. M ¨oller, and B. Otto, “Data sharing fundamentals: characteristics and definition,” in Proceedings of the 56th Hawaii International Conference on System Sciences , 2023

    2023

  18. [23]

    Big data privacy: challenges to privacy principles and models,

    J. Soria-Comas and J. Domingo-Ferrer, “Big data privacy: challenges to privacy principles and models,” Data science and engineering , vol. 1, no. 1, pp. 21–28, 2016

    2016

  19. [24]

    On the security of the tls protocol: A systematic analysis,

    H. Krawczyk, K. G. Paterson, and H. Wee, “On the security of the tls protocol: A systematic analysis,” in Annual Cryptology Conference. Springer, 2013, pp. 429–448

    2013

  20. [25]

    Enabling personal consent in databases,

    G. Konstantinidis, J. Holt, and A. Chapman, “Enabling personal consent in databases,” Proceedings of the VLDB Endowment , vol. 15, no. 2, pp. 375–387, 2021

    2021

  21. [26]

    Data governance: A conceptual framework, structured review, and research agenda,

    R. Abraham, J. Schneider, and J. V om Brocke, “Data governance: A conceptual framework, structured review, and research agenda,” International journal of information management , vol. 49, pp. 424– 438, 2019

    2019

  22. [27]

    Data governance: Organizing data for trustworthy artificial intelligence,

    M. Janssen, P. Brous, E. Estevez, L. S. Barbosa, and T. Janowski, “Data governance: Organizing data for trustworthy artificial intelligence,” Government information quarterly , vol. 37, no. 3, p. 101493, 2020. 28

    2020

  23. [28]

    Toward a research framework to conceptualize data as a factor of production: The data marketplace perspective,

    L. Huang, Y . Dou, Y . Liu, J. Wang, G. Chen, X. Zhang, and R. Wang, “Toward a research framework to conceptualize data as a factor of production: The data marketplace perspective,” Fundamental Research, vol. 1, no. 5, pp. 586–594, 2021

    2021

  24. [29]

    A marketplace for data: An algorithmic solution,

    A. Agarwal, M. Dahleh, and T. Sarkar, “A marketplace for data: An algorithmic solution,” in Proceedings of the 2019 ACM Conference on Economics and Computation , 2019, pp. 701–726

    2019

  25. [30]

    Agora: A privacy-aware data marketplace,

    V . Koutsos, D. Papadopoulos, D. Chatzopoulos, S. Tarkoma, and P. Hui, “Agora: A privacy-aware data marketplace,” IEEE Transactions on Dependable and Secure Computing , vol. 19, no. 6, pp. 3728–3740, 2021

    2021

  26. [31]

    Model-based data markets: a multi-broker game theoretic approach,

    Y . Ma, X. Jiang, E. W. Wu, L.-D. Ib´a˜nez, and J. Shi, “Model-based data markets: a multi-broker game theoretic approach,” in 2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) . IEEE, 2024, pp. 2293–2301

    2024

  27. [32]

    Data quality assessment,

    L. L. Pipino, Y . W. Lee, and R. Y . Wang, “Data quality assessment,” Communications of the ACM , vol. 45, no. 4, pp. 211–218, 2002

    2002

  28. [33]

    Methodolo- gies for data quality assessment and improvement,

    C. Batini, C. Cappiello, C. Francalanci, and A. Maurino, “Methodolo- gies for data quality assessment and improvement,” ACM computing surveys (CSUR), vol. 41, no. 3, pp. 1–52, 2009

    2009

  29. [34]

    Scaling and assessment of data quality,

    P. Evans, “Scaling and assessment of data quality,” Biological crystal- lography, vol. 62, no. 1, pp. 72–82, 2006

    2006

  30. [35]

    Blockchain-empowered trustworthy data sharing: Fundamentals, applications, and challenges,

    T. L. Nguyen, L. Nguyen, T. Hoang, D. Bandara, Q. Wang, Q. Lu, X. Xu, L. Zhu, and S. Chen, “Blockchain-empowered trustworthy data sharing: Fundamentals, applications, and challenges,” ACM Computing Surveys, vol. 57, no. 8, pp. 1–36, 2025

    2025

  31. [36]

    Data quality for machine learning tasks,

    N. Gupta, S. Mujumdar, H. Patel, S. Masuda, N. Panwar, S. Bandy- opadhyay, S. Mehta, S. Guttula, S. Afzal, R. Sharma Mittal et al., “Data quality for machine learning tasks,” in Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining , 2021, pp. 4040–4041

    2021

  32. [37]

    Can you trust wikidata?

    V . Santos, D. Schwabe, and S. Lifschitz, “Can you trust wikidata?” Semantic Web, no. Preprint, pp. 1–22, 2024

    2024

  33. [38]

    The effects of data quality on machine learning performance,

    L. Budach, M. Feuerpfeil, N. Ihde, A. Nathansen, N. Noack, H. Pat- zlaff, F. Naumann, and H. Harmouch, “The effects of data quality on machine learning performance,” arXiv preprint arXiv:2207.14529 , 2022

    2022 arXiv

  34. [39]

    Spatial data quality in the internet of things: Management, exploitation, and prospects,

    H. Li, H. Lu, C. S. Jensen, B. Tang, and M. A. Cheema, “Spatial data quality in the internet of things: Management, exploitation, and prospects,” ACM Computing Surveys (CSUR), vol. 55, no. 3, pp. 1–41, 2022

    2022

  35. [40]

    Policy auditing over incomplete logs: theory, implementation and applications,

    D. Garg, L. Jia, and A. Datta, “Policy auditing over incomplete logs: theory, implementation and applications,” in Proceedings of the 18th ACM conference on Computer and communications security , 2011, pp. 151–162

    2011

  36. [41]

    Automating audit with policy inference,

    A. Bichhawat, M. Fredrikson, and J. Yang, “Automating audit with policy inference,” in 2021 IEEE 34th Computer Security Foundations Symposium (CSF). IEEE, 2021, pp. 1–16

    2021

  37. [42]

    {PrivGuard}: Privacy regulation com- pliance made easier,

    L. Wang, U. Khan, J. Near, Q. Pang, J. Subramanian, N. Somani, P. Gao, A. Low, and D. Song, “ {PrivGuard}: Privacy regulation com- pliance made easier,” in 31st USENIX Security Symposium (USENIX Security 22), 2022, pp. 3753–3770

    2022

  38. [43]

    Reputation-aware data fusion and malicious participant detection in mobile crowdsensing,

    Y . Tang, S. Tasnim, N. Pissinou, S. Iyengar, and A. Shahid, “Reputation-aware data fusion and malicious participant detection in mobile crowdsensing,” in 2018 IEEE International Conference on Big Data (Big Data) . IEEE, 2018, pp. 4820–4828

    2018

  39. [44]

    Reputation-based federated learning for secure wireless networks,

    Z. Song, H. Sun, H. H. Yang, X. Wang, Y . Zhang, and T. Q. Quek, “Reputation-based federated learning for secure wireless networks,” IEEE Internet of Things Journal , vol. 9, no. 2, pp. 1212–1226, 2021

    2021

  40. [45]

    Resource optimization of mab-based reputation management for data trading in vehicular edge computing,

    H. Xiao, L. Cai, J. Feng, Q. Pei, and W. Shi, “Resource optimization of mab-based reputation management for data trading in vehicular edge computing,” IEEE Transactions on Wireless Communications , vol. 22, no. 8, pp. 5278–5290, 2023

    2023

  41. [46]

    Trust management for the semantic web,

    M. Richardson, R. Agrawal, and P. Domingos, “Trust management for the semantic web,” in International semantic Web conference . Springer, 2003, pp. 351–368

    2003

  42. [47]

    Reputation management in the age of the world-wide web,

    C. Tennie, U. Frith, and C. D. Frith, “Reputation management in the age of the world-wide web,” Trends in cognitive sciences , vol. 14, no. 11, pp. 482–488, 2010

    2010

  43. [48]

    From data asset to data product–the role of the data provider in the enterprise data marketplace,

    R. Eichler, C. Gr ¨oger, E. Hoos, H. Schwarz, and B. Mitschang, “From data asset to data product–the role of the data provider in the enterprise data marketplace,” in Symposium and Summer School on Service- Oriented Computing. Springer, 2022, pp. 119–138

    2022

  44. [49]

    Trust me, i’m an in- termediary! exploring data intermediation services,

    J. C. Schweihoff, I. Jussen, and F. M ¨oller, “Trust me, i’m an in- termediary! exploring data intermediation services,” in International Conference on Wirtschaftsinformatik. Springer, 2023, pp. 79–96

    2023

  45. [50]

    Protecting data markets from strategic buyers,

    R. Castro Fernandez, “Protecting data markets from strategic buyers,” in Proceedings of the 2022 International Conference on Management of Data, 2022, pp. 1755–1769

    2022

  46. [51]

    Trustful data sharing in the forest-based sector- opportunities and challenges for a data trustee,

    L. Schinke, M. Hoppen, A. Atanasyan, X. Gong, F. Heinze, K. Stollen- werk, and J. Roßmann, “Trustful data sharing in the forest-based sector- opportunities and challenges for a data trustee,” in VLDB Workshops, 2023

    2023

  47. [52]

    Data protection impact assessment: A tool for account- ability and the unclarified concept of ‘high risk’in the general data protection regulation,

    K. Demetzou, “Data protection impact assessment: A tool for account- ability and the unclarified concept of ‘high risk’in the general data protection regulation,” Computer Law & Security Review, vol. 35, no. 6, p. 105342, 2019

    2019

  48. [53]

    Big data challenge: a data management perspective,

    J. Chen, Y . Chen, X. Du, C. Li, J. Lu, S. Zhao, and X. Zhou, “Big data challenge: a data management perspective,” Frontiers of computer Science, vol. 7, pp. 157–164, 2013

    2013

  49. [54]

    Dataset search: a survey,

    A. Chapman, E. Simperl, L. Koesten, G. Konstantinidis, L.-D. Ib ´a˜nez, E. Kacprzak, and P. Groth, “Dataset search: a survey,” The VLDB Journal, vol. 29, no. 1, pp. 251–272, 2020

    2020

  50. [55]

    Dataset discovery and exploration: A survey,

    N. W. Paton, J. Chen, and Z. Wu, “Dataset discovery and exploration: A survey,” ACM Computing Surveys , vol. 56, no. 4, pp. 1–37, 2023

    2023

  51. [56]

    A data sharing agreement framework,

    V . Swarup, L. Seligman, and A. Rosenthal, “A data sharing agreement framework,” in International Conference on Information Systems Se- curity. Springer, 2006, pp. 22–36

    2006

  52. [57]

    Personal Data Protection Act (PDPA),

    Parliament of Singapore, “Personal Data Protection Act (PDPA),”

  53. [58]

    General Data Protection Regulation (GDPR),

    European Parliament and Council of the European Union, “General Data Protection Regulation (GDPR),” 2018. [Online]. Available: https://gdpr-info.eu/

    2018

  54. [59]

    Data Protection Act 2018,

    UK Parliament, “Data Protection Act 2018,” 2018. [Online]. Available: https://www.legislation.gov.uk/ukpga/2018/12/contents/enacted

    2018

  55. [60]

    California Consumer Privacy Act (CCPA),

    California State Legislature, “California Consumer Privacy Act (CCPA),” 2020. [Online]. Available: https://oag.ca.gov/privacy/ccpa

    2020

  56. [61]

    A comparison of data protection legislation and policies across the eu,

    B. Custers, F. Dechesne, A. M. Sears, T. Tani, and S. Van der Hof, “A comparison of data protection legislation and policies across the eu,” Computer Law & Security Review , vol. 34, no. 2, pp. 234–243, 2018

    2018

  57. [62]

    A smart contract- based dynamic consent management system for personal data usage under gdpr,

    M. M. Merlec, Y . K. Lee, S.-P. Hong, and H. P. In, “A smart contract- based dynamic consent management system for personal data usage under gdpr,” Sensors, vol. 21, no. 23, p. 7994, 2021

    2021

  58. [63]

    A privacy agreement model for web services,

    S. Benbernou, H. Meziane, Y . H. Li, and M.-S. Hacid, “A privacy agreement model for web services,” in IEEE International Conference on Services Computing (SCC 2007) . IEEE, 2007, pp. 196–203

    2007

  59. [64]

    Representing consent and policies for compliance,

    P. A. Bonatti, L. Sauro, and J. Langens, “Representing consent and policies for compliance,” in 2021 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW). IEEE, 2021, pp. 283– 291

    2021

  60. [65]

    Control, trust and the sharing of health information: the limits of trust,

    S. Holm, T. B. Kristiansen, and T. Ploug, “Control, trust and the sharing of health information: the limits of trust,” Journal of medical ethics , vol. 47, no. 12, pp. e35–e35, 2021

    2021

  61. [66]

    Enabling privacy-aware interoperable and quality iot data sharing with context,

    T. R. Chhetri, C. K. Dehury, B. Varghese, A. Fensel, S. N. Srirama, and R. J. DeLong, “Enabling privacy-aware interoperable and quality iot data sharing with context,” Future Generation Computer Systems , vol. 157, pp. 164–179, 2024

    2024

  62. [67]

    A consent-based privacy-compliant personal data-sharing system,

    H. Park, H. Oh, and J. K. Choi, “A consent-based privacy-compliant personal data-sharing system,” IEEE Access, 2023

    2023

  63. [68]

    Distributed ledger technology,

    X. Liu, B. Farahani, and F. Firouzi, “Distributed ledger technology,” in Intelligent Internet of Things . Springer, 2020, pp. 393–431

    2020

  64. [69]

    Blockchain based distributed trust management in iot and iiot: a survey,

    A. Lahbib, K. Toumi, A. Laouiti, and S. Martin, “Blockchain based distributed trust management in iot and iiot: a survey,” The Journal of Supercomputing, pp. 1–53, 2024

    2024

  65. [70]

    Privacy-preserving reputation systems based on blockchain and other cryptographic building blocks: A survey,

    O. Hasan, L. Brunie, and E. Bertino, “Privacy-preserving reputation systems based on blockchain and other cryptographic building blocks: A survey,” ACM Computing Surveys (CSUR) , vol. 55, no. 2, pp. 1–37, 2022

    2022

  66. [71]

    Bitcoin: A peer-to-peer electronic cash system,

    S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” 2008. [Online]. Available: https://assets.pubpub.org/d8wct41f/ 31611263538139.pdf

    2008

  67. [72]

    Security and privacy on blockchain,

    R. Zhang, R. Xue, and L. Liu, “Security and privacy on blockchain,” ACM Computing Surveys (CSUR) , vol. 52, no. 3, pp. 1–34, 2019

    2019

  68. [73]

    A survey of distributed consensus protocols for blockchain networks,

    Y . Xiao, N. Zhang, W. Lou, and Y . T. Hou, “A survey of distributed consensus protocols for blockchain networks,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 1432–1465, 2020

    2020

  69. [74]

    The tangle,

    S. Popov, “The tangle,” White paper , vol. 1, no. 3, p. 30, 2018. [Online]. Available: http://cryptoverze.s3.us-east-2.amazonaws.com/ wp-content/uploads/2018/11/10012054/IOTA-MIOTA-Whitepaper.pdf

    2018

  70. [75]

    Trust in edge-based internet of things architectures: state of the art and research challenges,

    L. Fotia, F. Delicato, and G. Fortino, “Trust in edge-based internet of things architectures: state of the art and research challenges,” ACM Computing Surveys, vol. 55, no. 9, pp. 1–34, 2023. 29

    2023

  71. [76]

    Formalizing and securing relationships on public networks,

    N. Szabo, “Formalizing and securing relationships on public networks,” First monday, 1997

    1997

  72. [77]

    Ethereum: A secure decentralised generalised transaction ledger,

    G. Wood et al. , “Ethereum: A secure decentralised generalised transaction ledger,” Ethereum project yellow paper, vol. 151, no. 2014, pp. 1–32, 2014. [Online]. Available: https://cryptodeep.ru/doc/paper.pdf

    2014

  73. [78]

    A comprehensive survey on smart contract construction and execution: paradigms, tools, and systems,

    B. Hu, Z. Zhang, J. Liu, Y . Liu, J. Yin, R. Lu, and X. Lin, “A comprehensive survey on smart contract construction and execution: paradigms, tools, and systems,” Patterns, vol. 2, no. 2,

  74. [79]

    An overview of blockchain smart contract execution mechanism,

    Y . Liu, J. He, X. Li, J. Chen, X. Liu, S. Peng, H. Cao, and Y . Wang, “An overview of blockchain smart contract execution mechanism,” Journal of Industrial Information Integration , p. 100674, 2024

    2024

  75. [80]

    Trust and reputation models for multiagent sys- tems,

    J. Granatyr, V . Botelho, O. R. Lessing, E. E. Scalabrin, J.-P. Barth `es, and F. Enembreck, “Trust and reputation models for multiagent sys- tems,” ACM Computing Surveys (CSUR) , vol. 48, no. 2, pp. 1–42, 2015

    2015

  76. [81]

    Trust management in social internet of things: Architectures, recent advancements, and future challenges,

    W. Z. Khan, Q.-u.-A. Arshad, S. Hakak, M. K. Khan, and Saeed-Ur- Rehman, “Trust management in social internet of things: Architectures, recent advancements, and future challenges,” IEEE Internet of Things Journal, vol. 8, no. 10, pp. 7768–7788, 2021

    2021

  77. [82]

    Towards trustwor- thy internet of things: A survey on trust management applications and schemes,

    A. Sharma, E. S. Pilli, A. P. Mazumdar, and P. Gera, “Towards trustwor- thy internet of things: A survey on trust management applications and schemes,” Computer Communications, vol. 160, pp. 475–493, 2020

    2020

  78. [83]

    Trust management for internet of things: A comprehensive study,

    L. Wei, Y . Yang, J. Wu, C. Long, and B. Li, “Trust management for internet of things: A comprehensive study,” IEEE Internet of Things Journal, vol. 9, no. 10, pp. 7664–7679, 2022

    2022

  79. [84]

    A survey on trust models in heterogeneous networks,

    J. Wang, Z. Yan, H. Wang, T. Li, and W. Pedrycz, “A survey on trust models in heterogeneous networks,” IEEE Communications Surveys & Tutorials, vol. 24, no. 4, pp. 2127–2162, 2022

    2022

  80. [85]

    Security, privacy, and decentralized trust management in vanets: a review of current research and future directions,

    M. AlMarshoud, M. Sabir Kiraz, and A. H. Al-Bayatti, “Security, privacy, and decentralized trust management in vanets: a review of current research and future directions,” ACM Computing Surveys , vol. 56, no. 10, pp. 1–39, 2024

    2024

  81. [86]

    Overview of the internet of things,

    T. ITU, “Overview of the internet of things,” Recommendation ITU-T Y, vol. 2060, p. 22, 2012

  82. [87]

    Decentralized trust management: Risk analysis and trust aggregation,

    X. Fan, L. Liu, R. Zhang, Q. Jing, and J. Bi, “Decentralized trust management: Risk analysis and trust aggregation,” ACM Computing Surveys (CSUR), vol. 53, no. 1, pp. 1–33, 2020

    2020

  83. [89]

    A survey on security and privacy of federated learning,

    V . Mothukuri, R. M. Parizi, S. Pouriyeh, Y . Huang, A. Dehghantanha, and G. Srivastava, “A survey on security and privacy of federated learning,” Future Generation Computer Systems , vol. 115, pp. 619– 640, 2021

    2021

  84. [90]

    Gentrust: A genetic trust management model for peer-to-peer systems,

    U. E. Tahta, S. Sen, and A. B. Can, “Gentrust: A genetic trust management model for peer-to-peer systems,” Applied Soft Computing, vol. 34, pp. 693–704, 2015

    2015

  85. [91]

    C-trust: A trust management system to improve fairness on circular p2p networks,

    A. Naghizadeh, B. Razeghi, E. Meamari, M. Hatamian, and R. E. Atani, “C-trust: A trust management system to improve fairness on circular p2p networks,” Peer-to-Peer Networking and Applications , vol. 9, pp. 1128–1144, 2016

    2016

  86. [93]

    Model of trust management for digital industry services. towards e-commerce 4.0,

    W. Bauer, N. Kryvinska, and J. Dorn, “Model of trust management for digital industry services. towards e-commerce 4.0,” arXiv preprint arXiv:2011.01523, 2020

    2011 arXiv

  87. [94]

    Guardian: Evaluating trust in online social networks with graph convolutional networks,

    W. Lin, Z. Gao, and B. Li, “Guardian: Evaluating trust in online social networks with graph convolutional networks,” in IEEE INFOCOM 2020-IEEE Conference on Computer Communications . IEEE, 2020, pp. 914–923

    2020

  88. [95]

    Holitrust-a holistic cross-domain trust management mechanism for service-centric internet of things,

    K. A. Awan, I. U. Din, M. Zareei, M. Talha, M. Guizani, and S. U. Jadoon, “Holitrust-a holistic cross-domain trust management mechanism for service-centric internet of things,” Ieee Access, vol. 7, pp. 52 191–52 201, 2019

    2019

  89. [96]

    Certificates and trust in electronic commerce,

    S. Wilson, “Certificates and trust in electronic commerce,” Information Management & computer security , vol. 5, no. 5, pp. 175–181, 1997

    1997

  90. [97]

    A survey on trust evaluation based on machine learning,

    J. Wang, X. Jing, Z. Yan, Y . Fu, W. Pedrycz, and L. T. Yang, “A survey on trust evaluation based on machine learning,”ACM Computing Surveys (CSUR), vol. 53, no. 5, pp. 1–36, 2020

    2020

  91. [98]

    Dtrust: Toward dynamic trust levels assessment in time-varying online social networks,

    J. Wen, N. Jiang, J. Li, X. Liu, H. Chen, Y . Ren, Z. Yuan, and Z. Tu, “Dtrust: Toward dynamic trust levels assessment in time-varying online social networks,” in IEEE INFOCOM 2023-IEEE Conference on Computer Communications. IEEE, 2023, pp. 1–10

    2023

  92. [99]

    Blockchain- based distributed management system for trust in vanet,

    Y . Inedjaren, M. Maachaoui, B. Zeddini, and J.-P. Barbot, “Blockchain- based distributed management system for trust in vanet,” Vehicular Communications, vol. 30, p. 100350, 2021

    2021

  93. [100]

    A decentralized public key infrastructure with identity retention,

    C. Fromknecht, D. Velicanu, and S. Yakoubov, “A decentralized public key infrastructure with identity retention,” Cryptology ePrint Archive , 2014

    2014

  94. [101]

    Advanced encryption standard (aes) algorithm to encrypt and decrypt data,

    A. M. Abdullah et al., “Advanced encryption standard (aes) algorithm to encrypt and decrypt data,” Cryptography and Network Security , vol. 16, no. 1, p. 11, 2017

    2017

  95. [102]

    The eigentrust algorithm for reputation management in p2p networks,

    S. D. Kamvar, M. T. Schlosser, and H. Garcia-Molina, “The eigentrust algorithm for reputation management in p2p networks,” in Proceedings of the 12th international conference on World Wide Web , 2003, pp. 640–651

    2003

  96. [103]

    A hybrid trust model using reinforcement learning and fuzzy logic,

    A. Aref and T. Tran, “A hybrid trust model using reinforcement learning and fuzzy logic,” Computational Intelligence, vol. 34, no. 2, pp. 515– 541, 2018

    2018

  97. [104]

    Enhancing trust in central differential privacy using zk-snarks and cryptographic hashes,

    R. Aziz, Y . Badr, and S. Bouzefrane, “Enhancing trust in central differential privacy using zk-snarks and cryptographic hashes,” in International Conference on Advanced Information Networking and Applications. Springer, 2025, pp. 163–176

    2025

  98. [105]

    Pseudo trust: Zero-knowledge authentication in anonymous p2ps,

    L. Lu, J. Han, Y . Liu, L. Hu, J.-P. Huai, L. Ni, and J. Ma, “Pseudo trust: Zero-knowledge authentication in anonymous p2ps,” IEEE Trans- actions on Parallel and Distributed Systems, vol. 19, no. 10, pp. 1325– 1337, 2008

    2008

  99. [106]

    Secure multi-party computation: theory, practice and applications,

    C. Zhao, S. Zhao, M. Zhao, Z. Chen, C.-Z. Gao, H. Li, and Y .-a. Tan, “Secure multi-party computation: theory, practice and applications,” Information Sciences, vol. 476, pp. 357–372, 2019

    2019

  100. [107]

    Fair and efficient secure multiparty computation with reputation systems,

    G. Asharov, Y . Lindell, and H. Zarosim, “Fair and efficient secure multiparty computation with reputation systems,” in International Con- ference on the Theory and Application of Cryptology and Information Security. Springer, 2013, pp. 201–220

    2013

  101. [108]

    Reversible data hiding in paillier cryptosystem,

    H.-T. Wu, Y .-m. Cheung, and J. Huang, “Reversible data hiding in paillier cryptosystem,” Journal of Visual Communication and Image Representation, vol. 40, pp. 765–771, 2016

    2016

  102. [109]

    Finquaxbot: enhancing trust and security in personalized investment and tax forecasting using homomorphic en- cryption and meta-reinforcement learning with explainability,

    I. Jency and R. A. Kumar, “Finquaxbot: enhancing trust and security in personalized investment and tax forecasting using homomorphic en- cryption and meta-reinforcement learning with explainability,” Expert Systems with Applications , p. 128136, 2025

    2025

  103. [110]

    A comparative analysis of trust models for safety applications in iot-enabled vehicular networks,

    F. Ahmad, A. Adnane, F. Kurugollu, and R. Hussain, “A comparative analysis of trust models for safety applications in iot-enabled vehicular networks,” in 2019 Wireless Days (WD) . IEEE, 2019, pp. 1–8

    2019

  104. [111]

    Using trust model to ensure reliable data acquisition in vanets,

    X. Yao, X. Zhang, H. Ning, and P. Li, “Using trust model to ensure reliable data acquisition in vanets,” Ad Hoc Networks , vol. 55, pp. 107–118, 2017

    2017

  105. [112]

    Beyond accuracy: What data quality means to data consumers,

    R. Y . Wang and D. M. Strong, “Beyond accuracy: What data quality means to data consumers,” Journal of management information sys- tems, vol. 12, no. 4, pp. 5–33, 1996

    1996

  106. [113]

    A quan- titative comparative study of data-oriented trust management schemes in internet of things,

    M. Ebrahimi, M. H. Tadayon, M. S. Haghighi, and A. Jolfaei, “A quan- titative comparative study of data-oriented trust management schemes in internet of things,” ACM Transactions on Management Information Systems (TMIS), vol. 13, no. 3, pp. 1–30, 2022

    2022

  107. [114]

    On data- centric trust establishment in ephemeral ad hoc networks,

    M. Raya, P. Papadimitratos, V . D. Gligor, and J.-P. Hubaux, “On data- centric trust establishment in ephemeral ad hoc networks,” in IEEE INFOCOM 2008-The 27th Conference on Computer Communications . IEEE, 2008, pp. 1238–1246

    2008

  108. [115]

    A trust management framework for service-oriented environments,

    W. Conner, A. Iyengar, T. Mikalsen, I. Rouvellou, and K. Nahrstedt, “A trust management framework for service-oriented environments,” in Proceedings of the 18th international conference on World wide web , 2009, pp. 891–900

    2009

  109. [116]

    State-of-the-art vanet trust models: Challenges and recommendations,

    H. Sateesh and P. Zavarsky, “State-of-the-art vanet trust models: Challenges and recommendations,” in 2020 11th IEEE Annual Infor- mation Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2020, pp. 0757–0764

    2020

  110. [117]

    A hybrid trust management heuristic for vanets,

    A. Mahmood, B. Butler, W. E. Zhang, Q. Z. Sheng, and S. A. Siddiqui, “A hybrid trust management heuristic for vanets,” in 2019 IEEE In- ternational Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 2019, pp. 748–752

    2019

  111. [118]

    Public trust in health information sharing: a measure of system trust,

    J. E. Platt, P. D. Jacobson, and S. L. Kardia, “Public trust in health information sharing: a measure of system trust,” Health services research, vol. 53, no. 2, pp. 824–845, 2018

    2018

  112. [119]

    Data centric trust evaluation and prediction framework for iot,

    U. Jayasinghe, A. Otebolaku, T.-W. Um, and G. M. Lee, “Data centric trust evaluation and prediction framework for iot,” in 2017 ITU kaleidoscope: challenges for a data-driven society (ITU K) . IEEE, 2017, pp. 1–7

    2017

  113. [120]

    Towards bayesian-based trust management for insider attacks in 30 healthcare software-defined networks,

    W. Meng, K.-K. R. Choo, S. Furnell, A. V . Vasilakos, and C. W. Probst, “Towards bayesian-based trust management for insider attacks in 30 healthcare software-defined networks,” IEEE Transactions on Network and Service Management , vol. 15, no. 2, pp. 761–773, 2018

    2018

  114. [121]

    C-deeptrust: A context-aware deep trust prediction model in online social networks,

    Q. Wang, W. Zhao, J. Yang, J. Wu, S. Xue, Q. Xing, and S. Y . Philip, “C-deeptrust: A context-aware deep trust prediction model in online social networks,” IEEE Transactions on neural networks and learning systems, vol. 34, no. 6, pp. 2767–2780, 2021

    2021

  115. [122]

    Human-in-the-loop data integration,

    G. Li, “Human-in-the-loop data integration,” Proceedings of the VLDB Endowment, vol. 10, no. 12, pp. 2006–2017, 2017

    2006

  116. [123]

    Merging trust in collaborative filtering to alleviate data sparsity and cold start,

    G. Guo, J. Zhang, and D. Thalmann, “Merging trust in collaborative filtering to alleviate data sparsity and cold start,” Knowledge-Based Systems, vol. 57, pp. 57–68, 2014

    2014

  117. [124]

    Propagation of trust and distrust,

    R. Guha, R. Kumar, P. Raghavan, and A. Tomkins, “Propagation of trust and distrust,” in Proceedings of the 13th international conference on World Wide Web, 2004, pp. 403–412

    2004

  118. [125]

    Calder and S

    A. Calder and S. Watkins, Information security risk management for ISO 27001/ISO 27002 . It Governance Ltd, 2019

    2019

  119. [126]

    A trust model based on a time decay factor for use in social networks,

    J. Jiang, H. Wang, and W. Li, “A trust model based on a time decay factor for use in social networks,” Computers & Electrical Engineering, vol. 85, p. 106706, 2020

    2020

  120. [127]

    Dynamic sliding window model for service reputation,

    X. Zhou, T. Ishida, and Y . Murakami, “Dynamic sliding window model for service reputation,” in 2015 IEEE International Conference on Services Computing. IEEE, 2015, pp. 25–32

    2015

  121. [128]

    Trusted dynamic level scheduling based on bayes trust model,

    W. Wei and Z. GuoSun, “Trusted dynamic level scheduling based on bayes trust model,” Science in China Series F: Information Sciences , vol. 50, no. 3, pp. 456–469, 2007

    2007

  122. [129]

    A dynamic trust model in internet of things,

    E. K. Wang, C.-M. Chen, D. Zhao, W. H. Ip, and K. L. Yung, “A dynamic trust model in internet of things,” Soft Computing , vol. 24, no. 8, pp. 5773–5782, 2020

    2020

  123. [130]

    An event-triggered framework for trust-mediated human-autonomy interaction,

    D. A. Williams, A. Chapman, and C. Manzie, “An event-triggered framework for trust-mediated human-autonomy interaction,” IEEE Transactions on Automatic Control , 2025

    2025

  124. [131]

    A trust-aware random walk model for return propensity estimation and consumer anomaly scoring in online shopping,

    X. Li, Y . Zhuang, Y . Fu, and X. He, “A trust-aware random walk model for return propensity estimation and consumer anomaly scoring in online shopping,” Science China Information Sciences, vol. 62, no. 5, p. 52101, 2019

    2019

  125. [132]

    Anomaly detection and trust authority in artificial intelligence and cloud computing,

    K. N. Qureshi, G. Jeon, and F. Piccialli, “Anomaly detection and trust authority in artificial intelligence and cloud computing,” Computer Networks, vol. 184, p. 107647, 2021

    2021

  126. [133]

    A novel trust management scheme based on dempster–shafer evidence theory for malicious nodes detection in wireless sensor networks,

    W. Zhang, S. Zhu, J. Tang, and N. Xiong, “A novel trust management scheme based on dempster–shafer evidence theory for malicious nodes detection in wireless sensor networks,” The Journal of Supercomputing, vol. 74, pp. 1779–1801, 2018

    2018

  127. [134]

    The pagerank citation ranking : Bringing order to the web,

    L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking : Bringing order to the web,” in The Web Conference , 1999. [Online]. Available: https://api.semanticscholar.org/CorpusID:1508503

    1999

  128. [135]

    Trustguard: Gnn- based robust and explainable trust evaluation with dynamicity support,

    J. Wang, Z. Yan, J. Lan, E. Bertino, and W. Pedrycz, “Trustguard: Gnn- based robust and explainable trust evaluation with dynamicity support,” IEEE Transactions on Dependable and Secure Computing , vol. 21, no. 5, pp. 4433–4450, 2024

    2024

  129. [136]

    Fairness in rankings and recommendations: an overview,

    E. Pitoura, K. Stefanidis, and G. Koutrika, “Fairness in rankings and recommendations: an overview,” The VLDB Journal , pp. 1–28, 2022

    2022

  130. [137]

    Ranking of direct trust, confidence, and reputation in an abstract system with unreliable components,

    R. Kiefhaber, R. Jahr, N. Msadek, and T. Ungerer, “Ranking of direct trust, confidence, and reputation in an abstract system with unreliable components,” in 2013 IEEE 10th International Conference on Ubiq- uitous Intelligence and Computing and 2013 IEEE 10th International Conf...

    2013

  131. [138]

    Machine learning based trust management framework for vehicular networks,

    H. El-Sayed, H. A. Ignatious, P. Kulkarni, and S. Bouktif, “Machine learning based trust management framework for vehicular networks,” Vehicular Communications, vol. 25, p. 100256, 2020

    2020

  132. [139]

    E. N. Barron, Game theory: an introduction . John Wiley & Sons, 2024

    2024

  133. [140]

    A game theory- based trust measurement model for social networks,

    Y . Wang, Z. Cai, G. Yin, Y . Gao, X. Tong, and Q. Han, “A game theory- based trust measurement model for social networks,” Computational social networks, vol. 3, pp. 1–16, 2016

    2016

  134. [141]

    Blockchain-enabled decentralized trust management and secure usage control of iot big data,

    M. Zhaofeng, W. Lingyun, W. Xiaochang, W. Zhen, and Z. Weizhe, “Blockchain-enabled decentralized trust management and secure usage control of iot big data,” IEEE Internet of Things Journal , vol. 7, no. 5, pp. 4000–4015, 2019

    2019

  135. [142]

    Data quality considerations for big data and machine learning: Going beyond data cleaning and trans- formations,

    V . Gudivada, A. Apon, and J. Ding, “Data quality considerations for big data and machine learning: Going beyond data cleaning and trans- formations,” International Journal on Advances in Software , vol. 10, no. 1, pp. 1–20, 2017

    2017

  136. [143]

    A lossless data-hiding based iot data authenticity model in edge-ai for connected living,

    M. S. Rahman, I. Khalil, X. Yi, M. Atiquzzaman, and E. Bertino, “A lossless data-hiding based iot data authenticity model in edge-ai for connected living,” ACM Transactions on Internet Technology (TOIT) , vol. 22, no. 3, pp. 1–25, 2021

    2021

  137. [144]

    Event- based data authenticity analytics for iot and blockchain-enabled esg disclosure,

    W. Chen, W. Wu, Z. Ouyang, Y . Fu, M. Li, and G. Q. Huang, “Event- based data authenticity analytics for iot and blockchain-enabled esg disclosure,” Computers & Industrial Engineering , vol. 190, p. 109992, 2024

    2024

  138. [145]

    Meeker, L

    W. Meeker, L. Escobar, and F. Pascual, Statistical Methods for Reliability Data , ser. Wiley Series in Probability and Statistics. Wiley, 2022. [Online]. Available: https://books.google.co.uk/books? id=seFaEAAAQBAJ

    2022

  139. [146]

    Different types of data and the validity of democracy measures,

    S.-E. Skaaning, “Different types of data and the validity of democracy measures,” Politics and Governance, vol. 6, no. 1, pp. 105–116, 2018

    2018

  140. [147]

    Information integrity: Are we there yet?

    K. Harley and R. Cooper, “Information integrity: Are we there yet?” ACM Computing Surveys (CSUR) , vol. 54, no. 2, pp. 1–35, 2021

    2021

  141. [148]

    Network con- sistent data association,

    A. Chakraborty, A. Das, and A. K. Roy-Chowdhury, “Network con- sistent data association,” IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 9, pp. 1859–1871, 2015

    2015

  142. [149]

    Towards federated learning approach to determine data relevance in big data,

    R. Doku, D. B. Rawat, and C. Liu, “Towards federated learning approach to determine data relevance in big data,” in 2019 IEEE 20th international conference on information reuse and integration for data science (IRI). IEEE, 2019, pp. 184–192

    2019

  143. [150]

    Effects of information relevance on decision making in complex environments,

    S. C. Streufert, “Effects of information relevance on decision making in complex environments,” Memory & Cognition , vol. 1, no. 3, pp. 224–228, 1973

    1973

  144. [151]

    Precision health data: Requirements, challenges and existing techniques for data security and privacy,

    C. Thapa and S. Camtepe, “Precision health data: Requirements, challenges and existing techniques for data security and privacy,” Computers in biology and medicine , vol. 129, p. 104130, 2021

    2021

  145. [152]

    Data sharing for precision medicine: policy lessons and future directions,

    A. Blasimme, M. Fadda, M. Schneider, and E. Vayena, “Data sharing for precision medicine: policy lessons and future directions,” Health Affairs, vol. 37, no. 5, pp. 702–709, 2018

    2018

  146. [153]

    Blockchain-based multiple groups data sharing with anonymity and traceability,

    H. Huang, X. Chen, and J. Wang, “Blockchain-based multiple groups data sharing with anonymity and traceability,” Science China Informa- tion Sciences, vol. 63, pp. 1–13, 2020

    2020

  147. [154]

    Blockchain- enhanced data sharing with traceable and direct revocation in iiot,

    K. Yu, L. Tan, M. Aloqaily, H. Yang, and Y . Jararweh, “Blockchain- enhanced data sharing with traceable and direct revocation in iiot,” IEEE transactions on industrial informatics , vol. 17, no. 11, pp. 7669– 7678, 2021

    2021

  148. [155]

    Enhanced authentication for de-duplication of big data on cloud storage system using machine learning approach,

    M. Akbar, I. Ahmad, M. Mirza, M. Ali, and P. Barmavatu, “Enhanced authentication for de-duplication of big data on cloud storage system using machine learning approach,” Cluster Computing, vol. 27, no. 3, pp. 3683–3702, 2024

    2024

  149. [156]

    Big data security and privacy,

    E. Bertino and E. Ferrari, “Big data security and privacy,” in A comprehensive guide through the Italian database research over the last 25 years . Springer, 2017, pp. 425–439

    2017

  150. [157]

    A survey on data leakage prevention systems,

    S. Alneyadi, E. Sithirasenan, and V . Muthukkumarasamy, “A survey on data leakage prevention systems,” Journal of Network and Computer Applications, vol. 62, pp. 137–152, 2016

    2016

  151. [158]

    Do not pull my data for resale: Protecting data providers using data retrieval pattern analysis,

    G. Wang, S. Xiang, Y . Duan, L. Huang, and W. Xu, “Do not pull my data for resale: Protecting data providers using data retrieval pattern analysis,” in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval , 2018, pp. 1053– 1056

    2018

  152. [159]

    Data protection by design tool for automated gdpr com- pliance verification based on semantically modeled informed consent,

    T. R. Chhetri, A. Kurteva, R. J. DeLong, R. Hilscher, K. Korte, and A. Fensel, “Data protection by design tool for automated gdpr com- pliance verification based on semantically modeled informed consent,” Sensors, vol. 22, no. 7, p. 2763, 2022

    2022

  153. [160]

    Data privacy, compliance, and security in cloud com- puting for finance,

    S. Yalamati, “Data privacy, compliance, and security in cloud com- puting for finance,” in Practical Applications of Data Processing, Algorithms, and Modeling . IGI Global, 2024, pp. 127–144

    2024

  154. [161]

    Peekaboo: A hub-based approach to enable transparency in data processing within smart homes,

    H. Jin, G. Liu, D. Hwang, S. Kumar, Y . Agarwal, and J. I. Hong, “Peekaboo: A hub-based approach to enable transparency in data processing within smart homes,” in 2022 IEEE symposium on security and privacy (SP) . IEEE, 2022, pp. 303–320

    2022

  155. [162]

    Open data innovation: Visualizations and process redesign as a way to bridge the transparency-accountability gap,

    S. Park and J. R. Gil-Garcia, “Open data innovation: Visualizations and process redesign as a way to bridge the transparency-accountability gap,” Government information quarterly , vol. 39, no. 1, p. 101456, 2022

    2022

  156. [163]

    The need for machine-processable agreements in health data management,

    G. Konstantinidis, A. Chapman, M. J. Weal, A. Alzubaidi, L. M. Ballard, and A. M. Lucassen, “The need for machine-processable agreements in health data management,” Algorithms, vol. 13, no. 4, p. 87, 2020

    2020

  157. [164]

    Trusted data sharing with flexible access control based on blockchain,

    X. Ma, C. Wang, and X. Chen, “Trusted data sharing with flexible access control based on blockchain,” Computer Standards & Interfaces, vol. 78, p. 103543, 2021

    2021

  158. [165]

    Secure data sharing in uav-assisted crowdsensing: Integration of blockchain and reputation incentive,

    L. Xie, Z. Su, N. Chen, and Q. Xu, “Secure data sharing in uav-assisted crowdsensing: Integration of blockchain and reputation incentive,” in 2021 IEEE Global Communications Conference (GLOBECOM) . IEEE, 2021, pp. 1–6. 31

    2021

  159. [166]

    Exploiting endorsement infor- mation and social influence for item recommendation,

    C.-T. Li, S.-D. Lin, and M.-K. Shan, “Exploiting endorsement infor- mation and social influence for item recommendation,” in Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval , 2011, pp. 1131–1132

    2011

  160. [167]

    Solving the cold start problem in trust management in iot,

    M. Bampatsikos, I. Politis, C. Xenakis, and S. CA Thomopoulos, “Solving the cold start problem in trust management in iot,” inProceed- ings of the 16th International Conference on Availability, Reliability and Security, 2021, pp. 1–9

    2021

  161. [168]

    P2dap—sybil attacks detection in vehicular ad hoc networks,

    T. Zhou, R. R. Choudhury, P. Ning, and K. Chakrabarty, “P2dap—sybil attacks detection in vehicular ad hoc networks,” IEEE journal on selected areas in communications , vol. 29, no. 3, pp. 582–594, 2011

    2011

  162. [169]

    Rep on the block: A next generation reputation system based on the blockchain,

    R. Dennis and G. Owen, “Rep on the block: A next generation reputation system based on the blockchain,” in 2015 10th Interna- tional Conference for Internet Technology and Secured Transactions (ICITST). IEEE, 2015, pp. 131–138

    2015

  163. [170]

    Online privacy concerns and privacy management: A meta-analytical review,

    L. Baruh, E. Secinti, and Z. Cemalcilar, “Online privacy concerns and privacy management: A meta-analytical review,” Journal of Communi- cation, vol. 67, no. 1, pp. 26–53, 2017

    2017

  164. [171]

    Public key infrastructure,

    T. Spies, “Public key infrastructure,” in Computer and Information Security Handbook. Elsevier, 2017, pp. 691–711

    2017

  165. [172]

    A privacy preserving distributed reputation mechanism,

    E. Anceaume, G. Guette, P. Lajoie-Mazenc, N. Prigent, and V . V . T. Tong, “A privacy preserving distributed reputation mechanism,” in2013 IEEE International Conference on Communications (ICC) . IEEE, 2013, pp. 1951–1956

    2013

  166. [173]

    Detecting bad- mouthing attacks on reputation systems using self-organizing maps,

    Z. Bankovi ´c, J. C. Vallejo, D. Fraga, and J. M. Moya, “Detecting bad- mouthing attacks on reputation systems using self-organizing maps,” in Computational Intelligence in Security for Information Systems: 4th International Conference, CISIS 2011, Held at IWANN 2011, Torremol...

    2011

  167. [174]

    A smart trust manage- ment method to detect on-off attacks in the internet of things,

    J. Caminha, A. Perkusich, M. Perkusich et al., “A smart trust manage- ment method to detect on-off attacks in the internet of things,” Security and Communication Networks , vol. 2018, 2018

    2018

  168. [175]

    Trust management for de- fending on-off attacks,

    Y . Chae, L. C. DiPippo, and Y . L. Sun, “Trust management for de- fending on-off attacks,” IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 4, pp. 1178–1191, 2014

    2014

  169. [176]

    Trust management in social internet of things: A taxonomy, open issues, and challenges,

    R. K. Chahal, N. Kumar, and S. Batra, “Trust management in social internet of things: A taxonomy, open issues, and challenges,” Computer Communications, vol. 150, pp. 13–46, 2020

    2020

  170. [177]

    Enhancing collusion resilience in reputation systems,

    H. Shen, Y . Lin, K. Sapra, and Z. Li, “Enhancing collusion resilience in reputation systems,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 8, pp. 2274–2287, 2015

    2015

  171. [178]

    Proof of location verification towards trustworthy collaborative multi-vendor robotic systems,

    E. W. Wu, M. Jurt, B. Holden, and Y . Jin, “Proof of location verification towards trustworthy collaborative multi-vendor robotic systems,” in 2024 IEEE International Conference on Industrial Technology (ICIT) . IEEE, 2024, pp. 1–8

    2024

  172. [179]

    A stochastic approach for an enhanced trust management in a decentralized healthcare environ- ment,

    C. Khalfaoui, S. Ayed, and M. Esseghir, “A stochastic approach for an enhanced trust management in a decentralized healthcare environ- ment,” in 2020 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) . IEEE, 2020, pp. 26–31

    2020

  173. [180]

    Blockchain-enabled secure and efficient data sharing scheme for trust management in healthcare smartphone network,

    R. Bhan, R. Pamula, P. Faruki, and J. Gajrani, “Blockchain-enabled secure and efficient data sharing scheme for trust management in healthcare smartphone network,” The Journal of Supercomputing , vol. 79, no. 14, pp. 16 233–16 274, 2023

    2023

  174. [181]

    Reliable federated learning for mobile networks,

    J. Kang, Z. Xiong, D. Niyato, Y . Zou, Y . Zhang, and M. Guizani, “Reliable federated learning for mobile networks,” IEEE Wireless Communications, vol. 27, no. 2, pp. 72–80, 2020

    2020

  175. [182]

    A reliable iot edge computing trust management mechanism for smart cities,

    B. Wang, M. Li, X. Jin, and C. Guo, “A reliable iot edge computing trust management mechanism for smart cities,” IEEE Access , vol. 8, pp. 46 373–46 399, 2020

    2020

  176. [183]

    A trust management system for fog computing services,

    S. O. Ogundoyin and I. A. Kamil, “A trust management system for fog computing services,” Internet of Things , vol. 14, p. 100382, 2021

    2021

  177. [184]

    A novel trust management model for edge computing,

    R. Latif, M. U. Ahmed, S. Tahir, S. Latif, W. Iqbal, and A. Ahmad, “A novel trust management model for edge computing,” Complex & Intelligent Systems, pp. 1–17, 2022

    2022

  178. [185]

    Crowdr- fbc: A distributed fog-blockchains for mobile crowdsourcing reputation management,

    Y . Yu, S. Liu, L. Guo, P. L. Yeoh, B. Vucetic, and Y . Li, “Crowdr- fbc: A distributed fog-blockchains for mobile crowdsourcing reputation management,” IEEE Internet of Things Journal, vol. 7, no. 9, pp. 8722– 8735, 2020

    2020

  179. [186]

    A blockchain- based vehicle-trust management framework under a crowdsourcing environment,

    D. Wang, X. Chen, H. Wu, R. Yu, and Y . Zhao, “A blockchain- based vehicle-trust management framework under a crowdsourcing environment,” in 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) . IEEE, 2020, pp. 1950–1955

    2020

  180. [187]

    An uncertainty- aware computational trust model considering the co-existence of trust and distrust in social networks,

    N. H. Aghdam, M. Ashtiani, and M. A. Azgomi, “An uncertainty- aware computational trust model considering the co-existence of trust and distrust in social networks,” Information Sciences , vol. 513, pp. 465–503, 2020

    2020

  181. [189]

    Takeaway trust: A market data perspective on reputation portability in electronic commerce,

    M. Hesse and T. Teubner, “Takeaway trust: A market data perspective on reputation portability in electronic commerce,” 2020

    2020

  182. [190]

    Privacy-preserving trust manage- ment method based on blockchain for cross-domain industrial iot,

    X. Wu, Y . Liu, J. Tian, and Y . Li, “Privacy-preserving trust manage- ment method based on blockchain for cross-domain industrial iot,” Knowledge-Based Systems, vol. 283, p. 111166, 2024

    2024

  183. [191]

    Evaluating attribution methods in machine learning interpretability,

    Q. E. A. Ratul, E. Serra, and A. Cuzzocrea, “Evaluating attribution methods in machine learning interpretability,” in 2021 IEEE Interna- tional Conference on Big Data (Big Data) . IEEE, 2021, pp. 5239– 5245

    2021

  184. [192]

    Legislative compliance assessment: framework, model and gdpr instantiation,

    S. Agarwal, S. Steyskal, F. Antunovic, and S. Kirrane, “Legislative compliance assessment: framework, model and gdpr instantiation,” in Privacy Technologies and Policy: 6th Annual Privacy Forum, APF 2018, Barcelona, Spain, June 13-14, 2018, Revised Selected Papers

    2018

  185. [193]

    A scalable consent, transparency and compliance architecture,

    S. Kirrane, J. D. Fern ´andez, W. Dullaert, U. Milosevic, A. Polleres, P. A. Bonatti, R. Wenning, O. Drozd, and P. Raschke, “A scalable consent, transparency and compliance architecture,” in The Semantic Web: ESWC 2018 Satellite Events: ESWC 2018 Satellite Events, Heraklion, C...

    2018

  186. [194]

    A survey of large language models,

    W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y . Hou, Y . Min, B. Zhang, J. Zhang, Z. Dong et al. , “A survey of large language models,” arXiv preprint arXiv:2303.18223 , 2023

    2023 arXiv

  187. [195]

    Towards mitigating llm hallucination via self reflection,

    Z. Ji, T. Yu, Y . Xu, N. Lee, E. Ishii, and P. Fung, “Towards mitigating llm hallucination via self reflection,” in Findings of the Association for Computational Linguistics: EMNLP 2023 , 2023, pp. 1827–1843

    2023

  188. [196]

    Springer, 2018, pp. 131–149

    2018

  189. [197]

    Modeling and mitigating human annotation errors to design efficient stream process- ing systems with human-in-the-loop machine learning,

    R. Pandey, H. Purohit, C. Castillo, and V . L. Shalin, “Modeling and mitigating human annotation errors to design efficient stream process- ing systems with human-in-the-loop machine learning,” International Journal of Human-Computer Studies , vol. 160, p. 102772, 2022

    2022

  190. [198]

    Explainable ai (xai): Core ideas, techniques, and solutions,

    R. Dwivedi, D. Dave, H. Naik, S. Singhal, R. Omer, P. Patel, B. Qian, Z. Wen, T. Shah, G. Morgan et al., “Explainable ai (xai): Core ideas, techniques, and solutions,” ACM computing surveys, vol. 55, no. 9, pp. 1–33, 2023

    2023

  191. [199]

    Multi-dimensional trust for context-aware services computing,

    A. Mousa, J. Bentahar, and O. Alam, “Multi-dimensional trust for context-aware services computing,” Expert Systems with Applications , vol. 172, p. 114592, 2021

    2021

  192. [200]

    Retrieval- augmented generation for knowledge-intensive nlp tasks,

    P. Lewis, E. Perez, A. Piktus, F. Petroni, V . Karpukhin, N. Goyal, H. K ¨uttler, M. Lewis, W.-t. Yih, T. Rockt ¨aschel et al. , “Retrieval- augmented generation for knowledge-intensive nlp tasks,” Advances in neural information processing systems , vol. 33, pp. 9459–9474, 2020

    2020

  193. [201]

    Llms for explainable ai: A compre- hensive survey,

    A. Bilal, D. Ebert, and B. Lin, “Llms for explainable ai: A compre- hensive survey,” arXiv preprint arXiv:2504.00125 , 2025

    2025 arXiv

  194. [202]

    Logparser-llm: Advancing efficient log parsing with large language models,

    A. Zhong, D. Mo, G. Liu, J. Liu, Q. Lu, Q. Zhou, J. Wu, Q. Li, and Q. Wen, “Logparser-llm: Advancing efficient log parsing with large language models,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ser. KDD ’24. New York, NY , USA: As...

    2024

  195. [203]

    A novel security- by-design methodology: Modeling and assessing security by slas with a quantitative approach,

    V . Casola, A. De Benedictis, M. Rak, and U. Villano, “A novel security- by-design methodology: Modeling and assessing security by slas with a quantitative approach,” Journal of Systems and Software , vol. 163, p. 110537, 2020

    2020

  196. [204]

    Self-verifiable content using decentralized identifiers,

    N. Fotiou, Y . Thomas, V . A. Siris, G. Xylomenos, and G. C. Poly- zos, “Self-verifiable content using decentralized identifiers,” Computer Networks, vol. 230, p. 109799, 2023

    2023

  197. [208]

    Stride-based threat modeling for cyber-physical systems,

    R. Khan, K. McLaughlin, D. Laverty, and S. Sezer, “Stride-based threat modeling for cyber-physical systems,” in 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) . IEEE, 2017, pp. 1–6

    2017

  198. [2012]

    Available: https://sso.agc.gov.sg/Act/PDPA2012

    [Online]. Available: https://sso.agc.gov.sg/Act/PDPA2012

  199. [2021]

    Available: https://www.cell.com/patterns/pdf/S2666- 3899(20)30243-9.pdf

    [Online]. Available: https://www.cell.com/patterns/pdf/S2666- 3899(20)30243-9.pdf

  200. [2022]

    Available: https://www.gov.uk/guidance/national-data- strategy

    [Online]. Available: https://www.gov.uk/guidance/national-data- strategy

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.