| Title | Privacy at Scale: Local Differential Privacy in Practice |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Cormode, Graham, Jha, Somesh, Kulkarni, Tejas, Li, Ninghui, Srivastava, Divesh, Wang, Tianhao |
| Conference Name | Proceedings of the 2018 International Conference on Management of Data |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4703-7 |
| Keywords | composability, Data collection, Differential privacy, local differential privacy, privacy, pubcrawl, Resiliency, Scalability |
| Abstract | 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. |
| URL | http://doi.acm.org/10.1145/3183713.3197390 |
| DOI | 10.1145/3183713.3197390 |
| Citation Key | cormode_privacy_2018 |
Privacy at Scale: Local Differential Privacy in Practice