Guest Editors' Introduction: Data Surveillance
IEEE Security and Privacy
A learning theory approach to non-interactive database privacy
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Differentially private aggregation of distributed time-series with transformation and encryption
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
A privacy-aware framework for participatory sensing
ACM SIGKDD Explorations Newsletter
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Real-time aggregate monitoring with differential privacy
Proceedings of the 21st ACM international conference on Information and knowledge management
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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.