FAST: differentially private real-time aggregate monitor with filtering and adaptive sampling

Abstract

Sharing aggregate statistics of private data can be of great value when data mining can be performed in real-time to understand important phenomena such as influenza outbreaks or traffic congestion. However, to this date there have been no tools for releasing real-time aggregated data with differential privacy, a strong and provable privacy guarantee. We propose FAST, a real-time system that allows differentially private aggregate sharing and time-series analytics. FAST employs a set of novel, adaptive strategies to improve the utility of shared/released data while guaranteeing the user-specified level of differential privacy. We will demonstrate the challenges and our solutions in the context of prepared data sets as well as live participation data dynamically collected among the SIGMOD'13 attendees.