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arXiv · 2511.18025

Correlated-Sequence Differential Privacy

Yifan Luo·Meng Zhang·Jin Xu·Junting Chen·Jianwei Huang
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Abstract

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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Yifan Luo, Meng Zhang, Jin Xu, Junting Chen, Jianwei Huang. 2025-11-22. Correlated-Sequence Differential Privacy. https://doi.org/10.1109/icccn65249.2025.11133721

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