Dynamic Pattern-Aware Privacy Protection for Data Streams via Local Differential Privacy

Time-series data offers significant potential for improving services but risks exposing sensitive information. Existing privacy mechanisms often distort critical temporal patterns, undermining data utility. To address this, we propose DP-ADSP, a dynamic pattern-aware data stream privacy protection mechanism that protects privacy while preserving essential time-series patterns. In particular, the core of our methodology lies in a dynamic pattern-aware importance sampling technique that intelligently determines whether to sample and perturb data points. To mitigate utility degradation from excessive perturbation, we develop an adaptive privacy budget allocation strategy that optimally balances privacy protection with data fidelity. Furthermore, we introduce an adaptive perturbation mechanism to enhance the privacy protection of data. We provide theoretical privacy guarantees and demonstrate DP-ADSP’s superiority over existing methods through extensive experiments on real-world datasets.