Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.17035 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910716126035968 |
|---|---|
| author | Hore, Gaurab McElroy, Tucker Roy, Anindya |
| author_facet | Hore, Gaurab McElroy, Tucker Roy, Anindya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17035 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Achieving Privacy Utility Balance for Multivariate Time Series Data Hore, Gaurab McElroy, Tucker Roy, Anindya Methodology Cryptography and Security 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. |
| title | Achieving Privacy Utility Balance for Multivariate Time Series Data |
| topic | Methodology Cryptography and Security |
| url | https://arxiv.org/abs/2411.17035 |