Privacy-Preserving for Dynamic Real-Time Published Data Streams Based on Local Differential Privacy

Real-time data collected from users can help various applications provide services, but there is a risk that sensitive information will be leaked. Existing local differential privacy (LDP)-based approaches mainly perturb each data point, which severely affects the time-series patterns, leading to sensitive information being leaked out from multiple consecutive significant patterns. In this article, we focus on dynamic data streams under honest but curious servers and propose a privacy-preserving method called PP-LDP to protect data privacy while preserving data stream patterns and improving the utility of published data. To this end, our approach consists of three main parts. First, we sample the points that can represent the data stream patterns by improving the popular least squares segmented linear fit method. Then, we use an adaptive budget allocation method to perturb the sampling points and provide
$w$
-event level privacy. Finally, we perform post-processing optimization of the data streams with Kalman filters to further improve the utility of the data streams. Extensive experimental results on realistic data sets show that our proposed scheme can not only protect the data streams’ privacy but also effectively preserve patterns and guarantee the utility of private data.