Local Differential Privacy for Data Streams

Abstract

The dynamic change, huge data size, and complex structure of the data stream have made it very difficult to be analyzed and protected in real-time. Traditional privacy protection models such as differential privacy which need to rely on the trusted servers or companies, and this will increase the uncertainty of protecting streaming privacy. In this paper, we propose a new privacy protection protocol for data streams under local differential privacy and w-event privacy, which makes it possible to keep up-to-date statistics over time, and it is still available when the third parties are untrusted. We use sliding window to collect the data streams in real-time, finding out the occurrence of significant moves, capturing the latest data distribution trend, and releasing the perturbed data streams report in time. This protocol provides a provable privacy guarantee, reduces computation and storage costs, and provides valuable statistical information. The experimental results of real datasets show that the proposed method can protect the privacy of the data streams and provide available statistical data at the same time.