Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Knowledge and Data Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
