差分隐私机制中随机性质量的实证分析

中文摘要 AI 辅助

差分隐私（DP）依赖精心校准的随机噪声来保护统计分析中的个人隐私。虽然理论研究已在弱随机性假设下分析了DP，但熵退化的实际后果仍鲜为人知。我们使用IBM的DiffPrivLib，对随机性质量如何影响差分隐私机制进行了系统的实证研究。我们引入了由成熟测试套件表征的逐步退化的熵源，从高质量的量子真随机数生成器（TRNGs）和密码学安全的伪随机数生成器（PRNGs）开始，一直到具有受控熵退化的系统性操纵源。通过在参考数据库上超过一百万次查询的重复实验以及补充统计测试，我们直接分析了经验隐私损失随机变量分布。我们的结果表明，当大约每8到16位中有1位被操纵时，DP机制能可靠地检测到偏差，检测灵敏度在比特级偏差和时间相关性之间显著变化。我们证明了分布异常的统计检测并不一定对应于实际隐私保证的违反。

英文摘要

Differential Privacy (DP) relies on carefully calibrated random noise to protect individual privacy in statistical analyses. While theoretical work has analyzed DP under weakened randomness assumptions, the practical consequences of entropy degradation remain poorly understood. We present a systematic empirical investigation of how randomness quality affects differential privacy mechanisms using IBM's DiffPrivLib. We introduce progressively degraded entropy sources characterized by established test suites, starting from high-quality quantum True Random Number Generators (TRNGs) and cryptographically secure Pseudo-Random Number Generators (PRNGs) down to systematically manipulated sources with controlled entropy degradation. Through repeated experiments over one million queries on a reference database and complementary statistical tests, we directly analyze empirical Privacy Loss Random Variable distributions. Our results demonstrate that DP mechanisms reliably detect deviations when approximately 1 bit in every 8 to 16 is manipulated, with detection sensitivity varying significantly between bit-level biases and temporal correlations. We demonstrate that statistical detection of distributional anomalies does not necessarily correspond to actual privacy guarantee violations.

发表机构

• Università degli Studi dell’Insubria（因苏布里亚大学）
• Random Power Srl（Random Power公司）

机构由 AI 辅助整理，请以论文原文为准。