Differential Privacy for Dynamic Data
Description
This Springer brief provides the necessary foundations to understand differential privacy and describes practical algorithms enforcing this concept for the publication of real-time statistics based on sensitive data. Several scenarios of interest are considered, depending on the kind of estimator to be implemented and the potential availability of prior public information about the data, which can be used greatly to improve the estimators' performance. The brief encourages the proper use of large datasets based on private data obtained from individuals in the world of the Internet of Things and participatory sensing. For the benefit of the reader, several examples are discussed to illustrate the concepts and evaluate the performance of the algorithms described. These examples relate to traffic estimation, sensing in smart buildings, and syndromic surveillance to detect epidemic outbreaks.
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Person
Jerome Le Ny is currently an Associate Professor in the Department of Electrical Engineering at Polytechnique Montreal, which he joined in May 2012, and a member of GERAD, a Canadian multi-university research center for decision systems. His research interests include control theory and stochastic signal processing, privacy and security for cyber-physical systems, and the design of large-scale intelligent automation, with applications to transportation systems, energy systems, and distributed robotics in particular. He has published papers on the topic of privacy-preserving dynamic data analysis since 2012 and co-organized invited sessions on the topic of privacy in systems and control at several Conference on Decision and Control (CDC) since then. He participated in a tutorial session on this topic at the CDC 2016. His group has received several research awards, including a best student paper award at CDC 2016, and a fellowship from the Humboldt Foundation in 2018.
Content
Chapter 1. Defining Privacy Preserving Data Analysis.- Chapter 2. Basic Differentially Private Mechanism.- Chapter 3. A Two-Stage Architecture for Differentially Private Filtering.- Chapter 4. Differentially Private Filtering for Stationary Stochastic Collective Signals.- Chapter 5. Differentially Private Kalman Filtering.- Chapter 6. Differentially Private Nonlinear Observers.- Chapter 7. Conclusion.