Privacy Preserving Wi-Fi Analytics

Mohammad Alaggan, INRIA Lyon, France

Abstract: As communications-enabled devices are becoming more ubiquitous, it becomes easier to track the movements of individuals through the radio signals broadcast by their devices. While there is a strong interest for physical analytics platforms to leverage this information for many purposes, this tracking also threatens the privacy of individuals. To solve this issue, we propose a privacy-preserving solution for collecting aggregate mobility patterns while satisfying the strong guarantee of epsilon-differential privacy at the same time. More precisely, we introduce a sanitization mechanism for efficient, privacy-preserving and non-interactive approximate distinct counting for physical analytics based on perturbed Bloom filters. We also extend and generalize previous approaches for estimating distinct count of events and joint events (i.e., intersection, and more generally t-out-of-n cardinalities). Finally, we experimentally evaluate our approach and compare it to previous ones on a real dataset.

Biography: Mohammad Alaggan is a postdoctoral researcher at Inria Lyon, France, currently on a leave of absence from his Assistant Professor position at Helwan University, Egypt. He obtained his PhD in privacy-preserving algorithms in 2013 at Inria Rennes, France, under supervision of Anne-Marie Kermarrec and Sebastien Gambs. His research interests include differential privacy, peer-to-peer systems and cryptographic protocols. 

==========================Partners & Sponsors ===========================