Correlated-Sequence Differential Privacy

2025-11-22

AI Summary

Multi-source time-series data inherently exhibit coupled correlations that violate the record independence assumption underlying differential privacy (DP), undermining privacy guarantees. Method: This paper proposes Coupled Sequence Differential Privacy (CSDP), a novel framework that models multivariate data streams as coupled Markov chains—revealing that strong coupling can suppress worst-case information leakage. CSDP introduces spectral-analysis-driven loose sensitivity bounds, correlation-aware sensitivity scaling, data aging, and FRAN—a feedback-regulated adaptive noise injection mechanism—enabling linear-time online privatization. Contribution/Results: Extensive experiments on healthcare and financial datasets demonstrate that CSDP improves the privacy–utility trade-off by approximately 50% over state-of-the-art correlation-aware DP methods and by two orders of magnitude over standard DP, while rigorously preserving sequential dependencies.

Technology Category

• Machine Learning: Privacy
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• Protecting privacy in correlated sequential data streams
• Quantifying attacker information gain from temporal correlations
• Adding minimal noise while maintaining data utility

Innovation

• CSDP framework for correlated sequential data privacy
• Coupling Markov Chain models multivariate streams
• FRAN mechanism combines aging and adaptive noise