# Achieving Privacy Utility Balance for Multivariate Time Series Data

arXiv: https://arxiv.org/abs/2411.17035 · HTML: https://mlsanity.com/paper/2411.17035/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2024.

## Built on by (most cited first, 1 of 1)

- [Privacy-Aware Time Series Synthesis via Public Knowledge Distillation](https://mlsanity.com/paper/2511.00700/) (2025)

## Abstract (arXiv)

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.