Pattern-Sensitive Local Differential Privacy for Finite-Range Time-Series Data in Mobile Crowdsensing

Abstract

Time-series data is crucial for the development of mobile crowdsensing (MCS). Participant’s privacy is one of the major concerns because MCS data often contain sensitive individual information. Existing privacy-preserving mechanisms for time-series data do not preserve salient patterns of the time series and take into account that the perturbed data may fall outside the valid data interval, leading to data distortion. To overcome these deficiencies, we first perform dynamic feature extraction and incorporate an adaptive sampling scheme that is sensitive to the distinction of short-term patterns and stable patterns. Then a Bounded Laplace (BLP) mechanism is adopted with a theoretical guarantee on the data perturbation range so as to address the issue of data going beyond the valid range. We establish theoretically that the proposed Adaptive Sampling and Randomized perturbation mechanism based on dynamic Temporal patterns (ASRT) satisfies the metric-based $w$ -event $\epsilon$ -LDP for privacy protection. Empirical results of extensive experiments on realworld datasets demonstrate that our proposed method is superior to existing protection mechanisms and the efficacy of our ASRT in enhancing data utility without introducing outliers.