提出可验证隐私保护的数据发布框架，兼顾安全与可用性。
REAEDP: Entropy-Calibrated Differentially Private Data Release with Formal Guarantees and Attack-Based Evaluation
- 基于熵校准的直方图与合成数据机制，实现可证明隐私保护
- 实测熵变化低于理论上限，攻击成功率趋近随机猜测
- 适合关注隐私可验证性的数据发布研究者与实践者
敏感数据发布面临成员推断、属性推断和记录链接等输出侧隐私威胁。本文提出REAEDP框架，结合熵校准直方图发布、合成数据生成机制及基于攻击的评估方法。理论上，推导了相邻直方图数据集下香农熵与瑞尼熵的显式敏感度边界，支持直方图统计的校准差分隐私发布。进一步分析了一种具有隐私测试结构的合成数据机制$/mathcal{F}$，在给定参数条件下满足形式化差分隐私保证。在多个公开表格数据集上，实测熵变化始终低于理论上限；标准拉普拉斯与高斯基线呈现相似趋势；随着隐私参数降低，成员推断与链接型攻击性能均趋近随机猜测水平。结果表明，REAEDP在测试条件下具备实用的隐私保护发布能力。源代码：https://github.com/mabo1215/REAEDP.git
原文摘要 · Abstract (English)
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
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