REAEDP: Entropy-Calibrated Differentially Private Data Release with Formal Guarantees and Attack-Based Evaluation

2026-03-13

AI Summary

This work addresses the challenge of releasing sensitive data under output-side privacy threats such as membership inference, attribute inference, and record linkage, which necessitate a balance between formal privacy guarantees and quantifiable utility. The authors propose the REAEDP framework, which introduces an entropy-calibrated differentially private histogram mechanism by deriving, for the first time, explicit upper bounds on the sensitivity of Shannon and Rényi entropies under neighboring histograms. Integrating synthetic data generation with evaluation based on real-world attacks, the framework achieves both theoretical rigor and practical usability. Experimental results demonstrate that observed entropy changes remain below the derived theoretical bounds, and as the privacy parameter decreases, attack success rates converge to random guessing, confirming high utility under strong privacy protection across multiple public tabular datasets.

Technology Category

• Machine Learning: Privacy
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Sensitive data release is vulnerable to output-side privacy threats such as membership inference, attribute inference, and record linkage. This creates a practical need for release mechanisms that provide formal privacy guarantees while preserving utility in measurable ways. We propose REAEDP, a differential privacy framework that combines entropy-calibrated histogram release, a synthetic-data release mechanism, and attack-based evaluation. On the theory side, we derive an explicit sensitivity bound for Shannon entropy, together with an extension to Rényi entropy, for adjacent histogram datasets, enabling calibrated differentially private release of histogram statistics. We further study a synthetic-data mechanism $\mathcal{F}$ with a privacy-test structure and show that it satisfies a formal differential privacy guarantee under the stated parameter conditions. On multiple public tabular datasets, the empirical entropy change remains below the theoretical bound in the tested regime, standard Laplace and Gaussian baselines exhibit comparable trends, and both membership-inference and linkage-style attack performance move toward random-guess behavior as the privacy parameter decreases. These results support REAEDP as a practically usable privacy-preserving release pipeline in the tested settings. Source code: https://github.com/mabo1215/REAEDP.git

Problem

• differential privacy
• data release
• privacy threats
• membership inference
• entropy

Innovation

• entropy-calibrated
• differential privacy
• synthetic data
• sensitivity bound
• attack-based evaluation