FLIP: A Utility Preserving Privacy Mechanism for Time Series

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2207.07721.pdf (414.01 KB)

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https://arxiv.org/abs/2207.07721

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https://doi.org/10.48550/arXiv.2207.07721
 http://hdl.handle.net/11603/25273

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UMBC Mathematics and Statistics Department
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https://orcid.org/0000-0001-6361-8295

Date

2022-07-15

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journal articles
preprints
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract

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.