Differential Privacy for Dynamic Data

ISBN-13: 9783030410384 / Angielski / Miękka / 2020 / 110 str.

Jerome Le Ny

Spis treści

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.

O autorze

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.