Institute of Electrical & Electronic Engineers
Sharing real-time aggregate statistics of private data is of great value to the public to perform data mining for understanding important phenomena, such as influenza outbreaks and traffic congestion. However, releasing time-series data with standard differential privacy mechanism has limited utility due to high correlation between data values. The authors propose FAST, a novel framework to release real-time aggregate statistics under differential privacy based on filtering and adaptive sampling. To minimize the overall privacy cost, FAST adaptively samples long time-series according to the detected data dynamics.