Achieving Privacy Utility Balance for Multivariate Time Series Data

TL;DR

This paper introduces a multivariate all-pass filtering method to privatize time series data, balancing privacy and utility, and demonstrates its effectiveness on simulated and real datasets.

Contribution

It extends all-pass filtering to multivariate time series with an optimization approach for improved privacy-utility trade-off.

Findings

Effective privacy-utility balance achieved

Applicable to real-world datasets like QWI

Outperforms traditional noise-addition methods

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.