Stateful Switch: Optimized Time Series Release with Local Differential Privacy

Qingqing Ye, Haibo Hu, Kai Huang, Man Ho Au, Qiao Xue

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Time series data have numerous applications in big data analytics. However, they often cause privacy issues when collected from individuals. To address this problem, most existing works perturb the values in the time series while retaining their temporal order, which may lead to significant distortion of the values. Recently, we propose TLDP model that perturbs temporal perturbation to ensure privacy guarantee while retaining original values. It has shown great promise to achieve significantly higher utility than value perturbation mechanisms in many time series analysis. However, its practicability is still undermined by two factors, namely, utility cost of extra missing or empty values, and inflexibility of privacy budget settings. To address them, in this paper we propose {\it switch} as a new two-way operation for temporal perturbation, as opposed to the one-way {\it dispatch} operation. The former inherently eliminates the cost of missing, empty or repeated values. Optimizing switch operation in a {\it stateful} manner, we then propose StaSwitch mechanism for time series release under TLDP. Through both analytical and empirical studies, we show that StaSwitch has significantly higher utility for the published time series than any state-of-the-art temporal- or value-perturbation mechanism, while allowing any combination of privacy budget settings.
Original languageEnglish
Title of host publicationProc. of the IEEE International Conference on Computer Communications
Place of PublicationNew York, USA
Publication statusPublished - May 2023


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