SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework | Paper Citation Record

Observation d613bbde-7807-4525-b0fe-063b5a7278c7 · outbound

This paper cites IEEE Access (2024).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework IEEE Access (2024)

Reference 1

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Observation 8f75deaf-af5b-4154-976c-96abfb48f553 · outbound

This paper cites https://argo-workflows.readthedocs.io/en/ (2024), Accessed: 23 September 2024.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework https://argo-workflows.readthedocs.io/en/ (2024), Accessed: 23 September 2024

Reference 2

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This paper cites In: CEUR WORKSHOP PROCEEDINGS.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework In: CEUR WORKSHOP PROCEEDINGS

Reference 3

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This paper cites Fog Computing: Theory and Practice pp.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Fog Computing: Theory and Practice pp

Reference 4

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This paper cites Building Machine Learn- ing and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners pp.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Building Machine Learn- ing and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners pp

Reference 5

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This paper cites Proceeding of the 28th International Conference on Enterprise Design, Operations and Computing (2024).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Proceeding of the 28th International Conference on Enterprise Design, Operations and Computing (2024)

Reference 6

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This paper cites Data 7(12), 178 (2022) 16 E.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Data 7(12), 178 (2022) 16 E

Reference 7

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Observation ca8b5df4-1bf0-47d1-9720-cede625bc568 · outbound

This paper cites A Survey of Data Synthesis Approaches.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework A Survey of Data Synthesis Approaches

Reference 8

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This paper cites Applied Sciences11(5), 2158 (2021).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Applied Sciences11(5), 2158 (2021)

Reference 9

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This paper cites an unresolved cited work.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Unresolved cited work

Reference 10

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This paper cites Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities

Reference 11

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Observation bf4c4201-3bea-411a-9177-3fe0063a95b8 · outbound

This paper cites In: Proceedings of the 12th International Conference on Management of Digital EcoSystems.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework In: Proceedings of the 12th International Conference on Management of Digital EcoSystems

Reference 12

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Unresolved cited work

Reference 13

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This paper cites Computers12(10), 198 (2023).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Computers12(10), 198 (2023)

Reference 14

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This paper cites In: Symposium and Summer School on Service-Oriented Computing.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework In: Symposium and Summer School on Service-Oriented Computing

Reference 15

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This paper cites O’Reilly Media (2020).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework O’Reilly Media (2020)

Reference 16

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This paper cites Master’s thesis, Tallinn University of Technology, Tallinn, Estonia (2024).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Master’s thesis, Tallinn University of Technology, Tallinn, Estonia (2024)

Reference 17

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Observation aedbf7b0-04e4-4042-a69a-1dffc71b7e12 · outbound

This paper cites Official Journal L 11959, 89–131 (2016).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Official Journal L 11959, 89–131 (2016)

Reference 18

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Unresolved cited work

Reference 19

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Unresolved cited work

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework eu/en/policies/data-act

Reference 21

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This paper cites Sin- gle Resolution Board v European Data Protection Supervisor.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Sin- gle Resolution Board v European Data Protection Supervisor

Reference 22

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This paper cites Internet Things19, 100514 (2022).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Internet Things19, 100514 (2022)

Reference 23

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This paper cites ACM Computing Surveys 57(1), 1–36 (2024).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework ACM Computing Surveys 57(1), 1–36 (2024)

Reference 24

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This paper cites Deploying AI in the Enterprise: IT Approaches for Design, DevOps, Governance, Change Management, Blockchain, and Quantum Computing pp.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Deploying AI in the Enterprise: IT Approaches for Design, DevOps, Governance, Change Management, Blockchain, and Quantum Computing pp

Reference 25

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework In: ARES

Reference 26

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Synthetic Data for Social Good

Reference 27

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Unresolved cited work

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Big Data & Society8(1), 2053951720982012 (2021)

Reference 29

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This paper cites Science 349(6245), 255–260 (2015).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Science 349(6245), 255–260 (2015)

Reference 30

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This paper cites In: Privacy and Identity Management.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework In: Privacy and Identity Management

Reference 31

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Unresolved cited work

Reference 32

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This paper cites https://www.kubeflow.org/docs/components/ pipelines/ (2024), Accessed: 23 September 2024.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework https://www.kubeflow.org/docs/components/ pipelines/ (2024), Accessed: 23 September 2024

Reference 33

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This paper cites https://kubernetes.io/ (2024), Accessed: 15 October 2024.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework https://kubernetes.io/ (2024), Accessed: 15 October 2024

Reference 34

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework In: Privacy and Identity Management

Reference 35

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Unresolved cited work

Reference 36

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Procedia Computer Science196, 263–271 (2022)

Reference 37

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Reference 38

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This paper cites In: Proceedings of the 2024 ACM/IEEE 44th Interna- tional Conference on Software Engineering: New Ideas and Emerging Results.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework In: Proceedings of the 2024 ACM/IEEE 44th Interna- tional Conference on Software Engineering: New Ideas and Emerging Results

Reference 39

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework https://minikube.sigs.k8s.io/docs/ (2024), Accessed: 12 November 2024

Reference 40

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework In: 2019 Amity international conference on artificial intelligence (AICAI)

Reference 41

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Reference 42

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Reference 43

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This paper cites IEEE Access9, 68028–68043 (2021).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework IEEE Access9, 68028–68043 (2021)

Reference 44

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework IEEE Access12, 88048–88074 (2024) 18 E

Reference 45

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Unresolved cited work

Reference 46

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework In: DSAA

Reference 47

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This paper cites Computational and structural biotechnology journal (2024).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Computational and structural biotechnology journal (2024)

Reference 48

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SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework In: SSDBM

Reference 49

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Observation ae3d1b1f-6355-4f31-8cde-dba89628776a · outbound

This paper cites In: International Conference on Enterprise Design, Operations, and Computing.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework In: International Conference on Enterprise Design, Operations, and Computing

Reference 50

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d98b2872-bc86-4053-8a1c-cb3d2b42f619 · outbound

This paper cites Knowledge-Based Systems194, 105596 (2020).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Knowledge-Based Systems194, 105596 (2020)

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T17:34:54.230609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57ed5715-4d15-4051-984c-58704f8af25a · outbound

This paper cites Fron- tiers Artif.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Fron- tiers Artif

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:57.475139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fd19a589-954b-4e52-9e80-f7f11f15ee3d · outbound

This paper cites IEEE Transactions on Software Engineering48(9), 3361–3377 (2021).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework IEEE Transactions on Software Engineering48(9), 3361–3377 (2021)

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:57.458938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0a28fd34-d6da-4b6e-95ce-47623a6fffa2 · outbound

This paper cites BMC medical research methodology19, 1–18 (2019).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework BMC medical research methodology19, 1–18 (2019)

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:57.439265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d75459ad-ec05-4ad0-986d-4f04e3240d2d · outbound

This paper cites Journal of Artificial Intelligence Research and Applications1(2), 292–332 (2021).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Journal of Artificial Intelligence Research and Applications1(2), 292–332 (2021)

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:57.422112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation addda449-4e4d-4b50-a4d8-22c95ed4af0e · outbound

This paper cites an unresolved cited work.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:34:57.406484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 66ca0cd6-908a-40a1-adea-e4516322817c · outbound

This paper cites In: ICLR.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework In: ICLR

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:57.391421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b46ed486-0233-41ce-8e69-7f1220476a05 · outbound

This paper cites Journal of Artificial Intelligence Research and Applications1(2), 58–88 (2021).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Journal of Artificial Intelligence Research and Applications1(2), 58–88 (2021)

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:57.209489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation edef197e-ee47-49ed-94a6-0b82dfa132a4 · outbound

This paper cites In: Privacy in Statistical Databases: UNESCO Chair in Data Privacy, International Conference, PSD 2018, Valencia, Spain, September 26–28, 2018, Proceedings.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework In: Privacy in Statistical Databases: UNESCO Chair in Data Privacy, International Conference, PSD 2018, Valencia, Spain, September 26–28, 2018, Proceedings

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:56.912440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 91239417-726e-4d8a-9795-e897debb69c8 · outbound

This paper cites Data9(1), 14 (2024).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Data9(1), 14 (2024)

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:56.665165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a6c70917-4139-4094-a200-154100f2a589 · outbound

This paper cites Available at SSRN 3762686 (2020).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Available at SSRN 3762686 (2020)

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:56.439425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9ad30324-8d05-4286-919e-9d884241b883 · outbound

This paper cites In:2020Internationalconferenceonartificialintelligenceandcomputerengineering (ICAICE).

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework In:2020Internationalconferenceonartificialintelligenceandcomputerengineering (ICAICE)

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:56.204956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7b03e3f9-0bec-4df5-aa32-8cfdfdb3f3c4 · outbound

This paper cites an unresolved cited work.

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:34:55.949161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers