An Event Grouping Approach for Infinite Stream with Differential Privacy

With the rapid advances in Internet technology, publishing real-time statistics data, in a privacy-preserving way, has led to a large body of research. The current state-of-the-art paradigm for privacy preserving with differential privacy on data stream is w-event privacy. But it neglects if only a few part of the elements of dataset change over time and others are substantially stabilize, then processing all the user data in specified timestamps will bring additional noise and reduce the utility of data. In this paper, a novel privacy preserving approach called G-event which follow the conventional use of w-event differential privacy is proposed. We group the statistics result at each timestamp based on difference calculation. Then the high difference group will publish more often than the similar group. We guarantee that all result with greater change will publish by adding noise, and the result with smaller change will be approximate with the corresponding lastly published statistics. Experiment using real-life dataset show that our approach improves the utility of data.