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

翻译：敏感数据发布易受成员推断、属性推断和记录链接等输出端隐私威胁。这产生了对在可度量方式下保持效用同时提供形式化隐私保证的发布机制的实际需求。我们提出REAEDP，一个结合熵校准直方图发布、合成数据发布机制与基于攻击评估的差分隐私框架。在理论层面，我们针对相邻直方图数据集推导了香农熵的显式敏感度边界及其对Rényi熵的扩展，从而实现了直方图统计量的校准差分隐私发布。我们进一步研究了一种具有隐私测试结构的合成数据机制$\mathcal{F}$，并证明其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，经验熵变化在测试范围内始终低于理论边界，标准拉普拉斯与高斯基线表现出可比趋势，且随着隐私参数减小，成员推断与链接式攻击性能均趋近随机猜测行为。这些结果支持REAEDP在测试场景中作为实际可用的隐私保护发布流程。源代码：https://github.com/mabo1215/REAEDP.git