Real-time aggregate monitoring with differential privacy

By Liyue Fan, Li Xiong. 2012

95 citations recorded by the source corpus.

Sharing real-time aggregate statistics of private data has given much benefit 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, an adaptive system to release real-time aggregate statistics under differential privacy with improved utility. To minimize overall privacy cost, FAST adaptively samples long time-series according to detected data dynamics. To improve the accuracy of data release per time stamp, filtering is used to predict data values at non-sampling points and to estimate true values from noisy observations at sampling points. Our experiments with three real data sets confirm that FAST improves the accuracy of time-series release and has excellent performance even under very small privacy cost.

Subfields: Artificial Intelligence, Computer Science Applications, Epidemiology

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Citations in the corpus

What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy (2017, background): “’s adaptive system [18] that uses a combination of filtering and sampling to increase the utility of differentially private aggregates.”
Differentially Private Event Sequences over Infinite Streams (2014, methodology): “In particular, the sub sequences that fall entirely in the scope of a sub mechanism may be more beneﬁted (e.g., from adaptive sampling as in [17]) than those that are split between two sub mechanisms.”
Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, background): “Therefore, protecting real-time data by adding desired amount of noise is one of the most desirable approach to transmit data without compromising privacy [212].”
Study : Privacy Preserving Release of Spatio-temporal Density in Paris (2014, background): “In [12], the authors propose a framework to release real-time aggregate statistics under DP based on filtering and adaptive sampling.”
Monitoring web browsing behavior with differential privacy (2014, methodology): “The ﬁrst algorithm extends our previously proposed FAST framework [14] and establishes a univariate time series model for the count series of every web page.”