Publishing Asynchronous Event Times with Pufferfish Privacy

Jiaxin Ding, Abhirup Ghosh, Rik Sarkar, Jie Gao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Publishing data from IoT devices raises concerns of leaking sensitive information. In this paper we consider the scenario of publishing data on events with timestamps. We formulate three privacy issues, namely, whether one can tell if an event happened or not; whether one can nail down the timestamp of an event within a given time interval; and whether one can infer the relative order of any two nearby events. We show that perturbation of event timestamps or adding fake events following carefully chosen distributions can address these privacy concerns. We present a rigorous study of privately publishing discrete event timestamps with privacy guarantees under the Pufferfish privacy framework. We also conduct extensive experiments to evaluate utility of the modified time series with real world location check-in and app usage data. Our mechanisms preserve the statistical utility of event data which are suitable for aggregate queries.
Original languageEnglish
Title of host publicationProceedings of The 18th International Conference on Distributed Computing in Sensor Systems 2022
Number of pages8
Publication statusAccepted/In press - 20 Mar 2022
EventThe 18th Annual International Conference on Distributed Computing in Sensor Systems, 2022 - Los Angeles, United States
Duration: 30 May 20221 Jun 2022
Conference number: 18

Conference

ConferenceThe 18th Annual International Conference on Distributed Computing in Sensor Systems, 2022
Abbreviated titleDCOSS 2022
Country/TerritoryUnited States
CityLos Angeles
Period30/05/221/06/22

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