Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/88240
Title: Local differential privacy : tools, challenges, and opportunities
Authors: Ye, Q 
Hu, H 
Issue Date: 2020
Source: Communications in computer and information science, 2020, v. 1155, p. 13-23
Abstract: Local Differential Privacy (LDP), where each user perturbs her data locally before sending to an untrusted party, is a new and promising privacy-preserving model. Endorsed by both academia and industry, LDP provides strong and rigorous privacy guarantee for data collection and analysis. As such, it has been recently deployed in many real products by several major software and Internet companies, including Google, Apple and Microsoft in their mainstream products such as Chrome, iOS, and Windows 10. Besides industry, it has also attracted a lot of research attention from academia. This tutorial first introduces the rationale of LDP model behind these deployed systems to collect and analyze usage data privately, then surveys the current research landscape in LDP, and finally identifies several open problems and research directions in this community.
Keywords: Data collection
Data analysis
Local differential privacy
Publisher: Springer
Journal: Communications in computer and information science 
ISSN: 1865-0929
EISSN: 1865-0937
DOI: 10.1007/978-981-15-3281-8_2
Description: Web Information Systems Engineering, WISE 2019, Workshop, Demo, and Tutorial, Hong Kong and Macau, China, January 19–22, 2020
Rights: © Springer Nature Singapore Pte Ltd. 2020
Ye Q., Hu H. (2020) Local Differential Privacy: Tools, Challenges, and Opportunities. In: U L., Yang J., Cai Y., Karlapalem K., Liu A., Huang X. (eds) Web Information Systems Engineering. WISE 2020. Communications in Computer and Information Science, vol 1155. Springer, Singapore.
The final authenticated version is available online at https://doi.org/10.1007/978-981-15-3281-8_2
Appears in Collections:Conference Paper

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