2022/07/15 by Tucker McElroy, Anindya Roy, McElroy, Tucker +3
Computer Science · Decision Sciences · #Cryptography and Security (cs.CR) #Data Quality and Management #FOS: Computer and information sciences #Methodology (stat.ME) #Privacy-Preserving Technologies in Data #Probability and Risk Models
paper · pdf · doi:10.48550/arxiv.2207.07721
openalex publication_date 2022/07/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.