PeGaSus: Data-Adaptive Differentially Private Stream Processing

Individuals are continually observed by an ever-increasing number of sensors that make up the Internet of Things. The resulting streams of data, which are analyzed in real time, can reveal sensitive personal information about individuals. Hence, there is an urgent need for stream processing solutions that can analyze these data in real time with provable guarantees of privacy and low error. We present PeGaSus, a new algorithm for differentially private stream processing. Unlike prior work that has focused on answering individual queries over streams, our algorithm is the first that can simultaneously support a variety of stream processing tasks -- counts, sliding windows, event monitoring -- over multiple resolutions of the stream. PeGaSus uses a Perturber to release noisy counts, a data-adaptive Perturber to identify stable uniform regions in the stream, and a query specific Smoother, which combines the outputs of the Perturber and Grouper to answer queries with low error. In a comprehensive study using a WiFi access point dataset, we empirically show that PeGaSus can answer continuous queries with lower error than the previous state-of-the-art algorithms, even those specialized to particular query types.