A tutorial on local differential privacy, “Privacy at scale: Local differential privacy in practice” prepared by G. Cormode, S. Jha, T. Kulkarni, N. Li, D. Srivastava, and T. Wang, will be presented at the ACM SIGMOD and SIGKDD conferences this summer. SIGMOD takes place in Houston, TX in June, and KDD will be in London, UK in August.
Local differential privacy (LDP), where users randomly perturb their inputs to provide plausible deniability of their data without the need for a trusted party, has been adopted recently by several major technology organizations, including Google, Apple and Microsoft. This tutorial aims to introduce the key technical underpinnings of these deployed systems, to survey current research that addresses related problems within the LDP model, and to identify relevant open problems and research directions for the community.
Draft slides are available, and video recordings should be available after they are presented.