  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. We 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. To
improve the accuracy of data release per time stamp, FAST predicts data values
at non-sampling points and corrects noisy observations at sampling points. Our
experiments with real-world as well as synthetic data sets confirm that FAST
improves the accuracy of released aggregates even under small privacy cost and
can be used to enable a wide range of monitoring applications.
