arxiv: 2604.24326 · v1 · submitted 2026-04-27 · 💻 cs.CR · cs.AI

Recognition: unknown

X-NegoBox: An Explainable Privacy-Budget Negotiation Framework for Secure Peer-to-Peer Energy Data Exchange

Poushali Sengupta , Sabita Maharjan , Frank Eliassen , Yan Zhang

Authors on Pith no claims yet

Pith reviewed 2026-05-08 02:49 UTC · model grok-4.3

classification 💻 cs.CR cs.AI

keywords privacy budget negotiationpeer-to-peer energy tradingexplainable decision makinglocal data confinementadaptive differential privacysmart grid securitydata sharing transparency

0 comments

The pith

X-NegoBox negotiates privacy budgets locally for each energy data request while generating readable explanations for decisions.

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

The paper introduces X-NegoBox to handle privacy during peer-to-peer energy data exchanges by confining all raw data to the individual prosumer's device. An autonomous protocol assesses each request against local factors including trust, sensitivity, purpose, history, and risk to set a fitting privacy budget or propose adjustments such as reduced detail. An explainable layer then supplies justifications for the outcome. Fixed privacy rules in current systems cannot adjust to changing conditions and leave participants without insight into choices, which discourages sharing needed for trading and forecasting.

Core claim

X-NegoBox keeps each prosumer's data inside a private local DataBox, runs the Autonomous Privacy Budget Negotiation Protocol to choose a privacy budget from trust, feature sensitivity, declared purpose, historical behavior, and risk-aware pricing, produces counter-offers such as lower resolution when needed, and applies the Explainable Agreement Layer to output human- and machine-readable reasons; experiments on realistic energy market settings report lower privacy leakage, higher acceptance rates, and greater interpretability.

What carries the argument

The Autonomous Privacy Budget Negotiation Protocol that evaluates local factors to set or adjust privacy budgets, paired with the Explainable Agreement Layer that produces justifications and the sandbox that executes requester code only on sanitized outputs.

If this is right

Raw data stays confined to the prosumer device at all times.
Only sanitized results leave the sandbox after local execution.
Each decision adapts to the specific request instead of applying one fixed policy.
Explanations accompany every accept, reject, or modified outcome.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The local sandbox execution pattern could reduce exposure in any peer-to-peer system that must run external code on private data.
Transparent justifications may help satisfy data-protection rules that require accountable decision making.
The same structure of local assessment plus counter-offers could transfer to other domains where sensitivity varies per request, such as shared sensor streams in buildings.

Load-bearing premise

The negotiation protocol can accurately judge trust, sensitivity, purpose, behavior, and risk from local data only without creating fresh privacy leaks or biased outcomes.

What would settle it

A test that shows the protocol's internal assessments leak information or that acceptance rates stay flat compared with fixed policies under identical energy market conditions would disprove the central benefit.

Figures

Figures reproduced from arXiv: 2604.24326 by Frank Eliassen, Poushali Sengupta, Sabita Maharjan, Yan Zhang.

**Figure 1.** Figure 1: X-NegoBox architecture enforcing a secure view at source ↗

**Figure 2.** Figure 2: Adaptive Privacy-Budget Negotiation Protocol. Negotiation view at source ↗

**Figure 3.** Figure 3: APBNP decision rates as a function of the initial privacy view at source ↗

**Figure 4.** Figure 4: Acceptance rate across configurations, show view at source ↗

**Figure 5.** Figure 5: Acceptance stability and privacy-regime transitions in X-N view at source ↗

read the original abstract

The decentralization of modern energy systems is transforming consumers into prosumers who continuously exchange data with aggregators, peers, and market operators. While such data is essential for peer-to-peer trading, demand response, and distributed forecasting, it can reveal sensitive household patterns and introduce privacy risks. Existing data sharing mechanisms rely on fixed policies or predefined differential privacy budgets, limiting their ability to adapt to variations in reliability, data sensitivity, and request purpose. As a result, prosumers rarely receive explanations for why a request is accepted, rejected, or modified, reducing trust and participation. To address these limitations, we propose X-NegoBox, an explainable negotiation framework for adaptive privacy budgeting and transparent decision making. Each prosumer data is managed locally within a private DataBox, where raw data remain confined. Incoming requests are processed by an Autonomous Privacy Budget Negotiation Protocol (APBNP), which determines an appropriate privacy budget based on trust, feature sensitivity, declared purpose, historical behavior, and risk-aware pricing. When needed, APBNP generates privacy-preserving counter-offers, such as reduced resolution or duration. An Explainable Agreement Layer (X-Contract) produces human- and machine-readable justifications for each decision. After agreement, requester code executes locally in a sandbox, and only sanitized outputs are shared. Experiments on realistic energy market settings show reduced privacy leakage, higher acceptance rates, and improved interpretability.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

X-NegoBox sketches an adaptive privacy negotiation setup for energy data sharing but the protocol mechanics and evaluation stay too thin to judge whether the local assessment actually works without new leaks.

read the letter

The paper's main contribution is a framework that replaces fixed privacy budgets with a negotiation process in peer-to-peer energy trading. Data stays in a local DataBox, APBNP sets budgets using trust, feature sensitivity, purpose, history, and risk, then X-Contract supplies readable justifications before sandboxed code runs and only sanitized results leave. That package targets a real gap where rigid policies reduce participation in decentralized systems, and the local-only plus explainable angle is a reasonable direction to explore.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes X-NegoBox, an explainable negotiation framework for adaptive privacy budgeting in peer-to-peer energy data exchange. Each prosumer maintains data locally in a DataBox; incoming requests are handled by the Autonomous Privacy Budget Negotiation Protocol (APBNP), which sets privacy budgets using local assessments of trust, feature sensitivity, purpose, historical behavior, and risk, and generates counter-offers when needed. An Explainable Agreement Layer (X-Contract) supplies human- and machine-readable justifications. After agreement, requester code executes in a sandbox and only sanitized outputs are released. Experiments on realistic energy market settings are reported to show reduced privacy leakage, higher acceptance rates, and improved interpretability.

Significance. If the local-only mechanisms for trust and risk assessment can be rigorously defined and proven secure, the framework would address a practical gap in decentralized energy systems by replacing fixed differential-privacy policies with adaptive, transparent negotiation. The emphasis on explainability and sandboxed execution could increase prosumer participation. However, the absence of concrete algorithms or security arguments leaves the claimed advantages over existing privacy-preserving data-sharing protocols unverified.

major comments (2)

[APBNP definition (abstract and protocol section)] The description of the Autonomous Privacy Budget Negotiation Protocol (APBNP) provides no algorithm, pseudocode, data structures, or formal argument showing how trust, historical behavior, feature sensitivity, and risk are computed strictly from local DataBox data without external queries, side-channel leaks, or unstated priors that could introduce bias. This property is load-bearing for the central claim of secure, adaptive privacy budgeting.
[Experimental evaluation] The experimental claims of reduced privacy leakage, higher acceptance rates, and improved interpretability are stated without any metrics, baselines, datasets, error bars, statistical tests, or implementation details. Consequently, the reported gains cannot be evaluated or reproduced.

minor comments (2)

[Introduction and framework overview] The invented entities (DataBox, APBNP, X-Contract) are introduced without explicit comparison to related concepts such as secure enclaves, existing negotiation protocols, or differential-privacy budgeting mechanisms.
[Framework description] Notation for privacy budgets, risk scores, and counter-offer parameters is used without formal definitions or equations.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and will revise the manuscript accordingly to strengthen the presentation of the APBNP protocol and the experimental evaluation.

read point-by-point responses

Referee: [APBNP definition (abstract and protocol section)] The description of the Autonomous Privacy Budget Negotiation Protocol (APBNP) provides no algorithm, pseudocode, data structures, or formal argument showing how trust, historical behavior, feature sensitivity, and risk are computed strictly from local DataBox data without external queries, side-channel leaks, or unstated priors that could introduce bias. This property is load-bearing for the central claim of secure, adaptive privacy budgeting.

Authors: We agree that the current manuscript describes APBNP primarily at a conceptual level. In the revised version we will add explicit pseudocode for the negotiation steps, data structures for local trust/sensitivity/risk scores, and a formal argument establishing that every computation uses only data resident in the DataBox. We will also specify the exact local formulas (e.g., historical-behavior counters and sensitivity weights) and demonstrate the absence of external queries or side-channel leakage paths. revision: yes
Referee: [Experimental evaluation] The experimental claims of reduced privacy leakage, higher acceptance rates, and improved interpretability are stated without any metrics, baselines, datasets, error bars, statistical tests, or implementation details. Consequently, the reported gains cannot be evaluated or reproduced.

Authors: We acknowledge that the experimental claims in the current version lack the required quantitative detail. The revised manuscript will report concrete metrics (e.g., average leakage reduction in bits, acceptance-rate percentages), explicit baselines (fixed differential-privacy policies and non-negotiated sharing), dataset descriptions (synthetic and real-world energy-market traces), error bars, statistical tests (t-tests or Wilcoxon signed-rank with p-values), and implementation specifics (sandbox configuration, X-Contract generation library, and simulation parameters). revision: yes

Circularity Check

0 steps flagged

No circularity: X-NegoBox is a novel framework construction without self-referential reductions

full rationale

The paper introduces X-NegoBox as a new explainable negotiation framework with APBNP for adaptive privacy budgeting and X-Contract for justifications. The provided text contains no equations, fitted parameters, derivations, or self-citations that reduce any claim to its own inputs by construction. All elements are presented as original protocol definitions operating on local DataBox data, with experimental outcomes described as empirical results rather than tautological predictions. No load-bearing step matches any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 3 invented entities

The proposal rests on several newly introduced components whose correctness and security properties are not independently evidenced in the provided abstract.

invented entities (3)

DataBox no independent evidence
purpose: Local container that keeps raw prosumer data confined
Core architectural element for privacy preservation
Autonomous Privacy Budget Negotiation Protocol (APBNP) no independent evidence
purpose: Determines privacy budget and generates counter-offers from trust, sensitivity, purpose, behavior, and risk factors
Central decision-making component
Explainable Agreement Layer (X-Contract) no independent evidence
purpose: Generates human- and machine-readable justifications for accept/reject/modify decisions
Provides transparency

pith-pipeline@v0.9.0 · 5568 in / 1324 out tokens · 59521 ms · 2026-05-08T02:49:15.731621+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

24 extracted references · 2 canonical work pages · 1 internal anchor

[1]

Electricity market design for the prosumer era,

Y . Parag and B. Sovacool, “Electricity market design for the prosumer era,”Nature Energy, vol. 1, no. 4, p. 16032, 2016

2016
[2]

Review on peer- to-peer energy trading,

C. Zhang, J. Wu, Y . Zhou, M. Cheng, and C. Long, “Review on peer- to-peer energy trading,”Energy Procedia, vol. 143, pp. 128–134, 2018. 9Ethics and Reproducibility:X-NegoBox follows privacy-by-design, keeping raw data local and releasing only DP outputs. Experiments use public/synthetic data and are reproducible (https://github.com/Poushali96/ X-NEGOBOX)...

2018
[3]

Nonintrusive appliance load monitoring,

G. W. Hart, “Nonintrusive appliance load monitoring,”Proceedings of the IEEE, vol. 80, no. 12, pp. 1870–1891, 1992

1992
[4]

Private memoirs of a smart meter,

A. Molina-Markham, P. Shenoy, K. Fu, E. Cecchet, and D. Irwin, “Private memoirs of a smart meter,” inProceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Build- ing, 2010, pp. 61–66

2010
[5]

Calibrating noise to sensitivity in private data analysis,

C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” inTheory of Cryptography Conference (TCC), 2006, pp. 265–284

2006
[6]

Deep learning with differential privacy,

M. Abadi, A. Chu, I. Goodfellow, B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” inProceedings of the ACM SIGSAC Conference on Computer and Communications Security, 2016, pp. 308–318

2016
[7]

Counterfactual explanations without opening the black box,

S. Wachter, B. Mittelstadt, and C. Russell, “Counterfactual explanations without opening the black box,”Harvard Journal of Law & Technology, vol. 31, no. 2, pp. 841–887, 2017

2017
[8]

Using peer-to-peer energy-trading platforms to incentivize prosumers,

T. Morstyn and M. McCulloch, “Using peer-to-peer energy-trading platforms to incentivize prosumers,”Nature Energy, vol. 3, no. 2, pp. 94–101, 2018

2018
[9]

Data-driven energy management in smart grids,

W. Konget al., “Data-driven energy management in smart grids,”IEEE Signal Processing Magazine, vol. 38, no. 1, pp. 65–77, 2021

2021
[10]

Smart meter data analytics for consumer privacy,

A. Gianiet al., “Smart meter data analytics for consumer privacy,”IEEE Security & Privacy, vol. 18, no. 2, pp. 56–65, 2020

2020
[11]

Revealing household characteristics from smart meter data,

C. Beckelet al., “Revealing household characteristics from smart meter data,” inProceedings of the 4th ACM International Conference on Future Energy Systems, 2014, pp. 145–156

2014
[12]

Privacy and human behavior in the age of informa- tion,

A. Acquistiet al., “Privacy and human behavior in the age of informa- tion,”Science, vol. 347, no. 6221, pp. 509–514, 2016

2016
[13]

Security analysis of emerging smart home applica- tions,

E. Fernandeset al., “Security analysis of emerging smart home applica- tions,” inIEEE Symposium on Security and Privacy, 2014, pp. 636–654

2014
[14]

Dwork and A

C. Dwork and A. Roth,The algorithmic foundations of differential privacy. Now Publishers, 2014

2014
[15]

Differential privacy for smart grid data: A survey,

A. Ghasemiet al., “Differential privacy for smart grid data: A survey,” IEEE Access, vol. 9, pp. 122 459–122 483, 2021

2021
[16]

Towards federated learning at scale,

K. Bonawitzet al., “Towards federated learning at scale,”Proceedings of MLSys, 2019

2019
[17]

Spying on the smart home,

N. Apthorpeet al., “Spying on the smart home,” inUSENIX Security Symposium, 2017, pp. 1063–1080

2017
[18]

General data protection regulation (gdpr),

European Union, “General data protection regulation (gdpr),” 2016

2016
[19]

Decentralizing privacy: Using blockchain to protect personal data,

G. Zyskind, O. Nathan, and A. Pentland, “Decentralizing privacy: Using blockchain to protect personal data,”IEEE Security & Privacy Workshops, 2015

2015
[20]

Adaptive privacy protection for smart grid data,

L. Fanet al., “Adaptive privacy protection for smart grid data,”IEEE Transactions on Smart Grid, vol. 11, no. 5, pp. 4204–4215, 2020

2020
[21]

Towards A Rigorous Science of Interpretable Machine Learning

F. Doshi-Velez and B. Kim, “Towards a rigorous science of interpretable machine learning,”arXiv preprint arXiv:1702.08608, 2017

work page internal anchor Pith review arXiv 2017
[22]

Membership inference attacks against machine learning models,

N. Li, W. Qardaji, and D. Su, “Membership inference attacks against machine learning models,”IEEE Symposium on Security and Privacy, 2016

2016
[23]

Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption.arXiv preprint arXiv:1711.10677, 2017

S. Hardy, W. Henecka, H. Ivey-Law, R. Nock, G. Patrini, B. Smith, and S. Thorne, “Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption,”arXiv preprint arXiv:1711.10677, 2017

work page arXiv 2017
[24]

Intel sgx explained,

V . Costan and S. Devadas, “Intel sgx explained,” inIACR Cryptology ePrint Archive, 2016

2016