Achieving Privacy Utility Balance for Multivariate Time Series Data

2024/11/26 by Gaurab Hore, Hore, Gaurab, Tucker McElroy +3 · 1 citation
Computer Science · Decision Sciences · Social Sciences · #Crime Patterns and Interventions #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Methodology (stat.ME) #Privacy-Preserving Technologies in Data #Probability and Risk Models

paper · pdf · doi:10.48550/arxiv.2411.17035

openalex publication_date 2024/11/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Utility-preserving data privatization is of utmost importance for data-producing agencies. The popular noise-addition privacy mechanism distorts autocorrelation patterns in time series data, thereby marring utility; in response, McElroy et al. (2023) introduced all-pass filtering (FLIP) as a utility-preserving time series data privatization method. Adapting this concept to multivariate data is more complex, and in this paper we propose a multivariate all-pass (MAP) filtering method, employing an optimization algorithm to achieve the best balance between data utility and privacy protection. To test the effectiveness of our approach, we apply MAP filtering to both simulated and real data, sourced from the U.S. Census Bureau's Quarterly Workforce Indicator (QWI) dataset.

Cited by

• Privacy-Aware Time Series Synthesis via Public Knowledge Distillation

Related

• FLIP: A Utility Preserving Privacy Mechanism for Time Series
• Protecting Time Series Data with Minimal Forecast Loss
• Formal Privacy Guarantees with Invariant Statistics
• Stateful Switch: Optimized Time Series Release with Local Differential Privacy
• Exploring the Tradeoff Between Privacy and Utility of Complete‐count Census Data Using a Multiobjective Optimization Approach