FLIP: A Utility Preserving Privacy Mechanism for Time Series

Autor:	McElroy, Tucker, Roy, Anindya, Hore, Gaurab
Rok vydání:	2022
Předmět:	Computer Science - Cryptography and Security Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	Guaranteeing privacy in released data is an important goal for data-producing agencies. There has been extensive research on developing suitable privacy mechanisms in recent years. Particularly notable is the idea of noise addition with the guarantee of differential privacy. There are, however, concerns about compromising data utility when very stringent privacy mechanisms are applied. Such compromises can be quite stark in correlated data, such as time series data. Adding white noise to a stochastic process may significantly change the correlation structure, a facet of the process that is essential to optimal prediction. We propose the use of all-pass filtering as a privacy mechanism for regularly sampled time series data, showing that this procedure preserves utility while also providing sufficient privacy guarantees to entity-level time series. Comment: 19 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.07721 Zobrazit plný text záznamu View this record from Arxiv