Details

Autor(en) / Beteiligte

• Cormode, Graham
• Jha, Somesh
• Kulkarni, Tejas
• Li, Ninghui
• Srivastava, Divesh
• Wang, Tianhao

Titel

Privacy at Scale: Local Differential Privacy in Practice

Ist Teil von

• Proceedings of the 2018 International Conference on Management of Data, 2018, p.1655-1658

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2018

Quelle

ACM Digital Library

Beschreibungen/Notizen

• 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.