针对多序列数据相关性，提出新型隐私保护框架CSDP，提升隐私与可用性平衡。
Correlated-Sequence Differential Privacy
- 用耦合马尔可夫链建模多序列相关性，量化攻击者获益
- 引入自适应噪声机制，实测隐私-效用比提升50%以上
- 适合医疗、金融等存在时间与跨序列关联的数据场景
从多个来源收集的数据流通常并非独立。数值随时间演变并相互影响。这种相关性在医疗、金融和智慧城市控制中有助于提升预测性能，但违反了大多数差分隐私（DP）机制所依赖的记录独立性假设。为在不牺牲数据效用的前提下恢复严格的隐私保障，我们提出了一种专为相关序列数据设计的隐私框架——相关序列差分隐私（CSDP）。CSDP解决两个关联挑战：量化攻击者通过时间与跨序列关联获得的额外信息，以及添加足够噪声以隐藏这些信息同时保持数据可用性。我们将多变量数据流建模为耦合马尔可夫链，推导出基于少数谱项的宽松泄漏界，并揭示一个反直觉结果：更强的耦合反而可通过分散扰动降低最坏情况下的泄漏。基于该边界，我们构建了新机制FRAN（新鲜度调节自适应噪声），结合数据老化、相关性感知敏感度缩放和拉普拉斯噪声，实现线性时间运行。在双序列数据集上的测试表明，相较于现有相关差分隐私方法，CSDP的隐私-效用权衡提升约50%，相比标准差分隐私方法提升两个数量级。
原文摘要 · Abstract (English)
Data streams collected from multiple sources are rarely independent. Values evolve over time and influence one another across sequences. These correlations improve prediction in healthcare, finance, and smart-city control yet violate the record-independence assumption built into most Differential Privacy (DP) mechanisms. To restore rigorous privacy guarantees without sacrificing utility, we introduce Correlated-Sequence Differential Privacy (CSDP), a framework specifically designed for preserving privacy in correlated sequential data. CSDP addresses two linked challenges: quantifying the extra information an attacker gains from joint temporal and cross-sequence links, and adding just enough noise to hide that information while keeping the data useful. We model multivariate streams as a Coupling Markov Chain, yielding the derived loose leakage bound expressed with a few spectral terms and revealing a counterintuitive result: stronger coupling can actually decrease worst-case leakage by dispersing perturbations across sequences. Guided by these bounds, we build the Freshness-Regulated Adaptive Noise (FRAN) mechanism--combining data aging, correlation-aware sensitivity scaling, and Laplace noise--that runs in linear time. Tests on two-sequence datasets show that CSDP improves the privacy-utility trade-off by approximately 50% over existing correlated-DP methods and by two orders of magnitude compared to the standard DP approach.
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