Visible to the public Privacy at Scale: Local Differential Privacy in Practice

TitlePrivacy at Scale: Local Differential Privacy in Practice
Publication TypeConference Paper
Year of Publication2018
AuthorsCormode, Graham, Jha, Somesh, Kulkarni, Tejas, Li, Ninghui, Srivastava, Divesh, Wang, Tianhao
Conference NameProceedings of the 2018 International Conference on Management of Data
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4703-7
Keywordscomposability, Data collection, Differential privacy, local differential privacy, privacy, pubcrawl, Resiliency, Scalability
AbstractLocal 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.
Citation Keycormode_privacy_2018