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2020 | OriginalPaper | Buchkapitel

Local Differential Privacy: Tools, Challenges, and Opportunities

verfasst von : Qingqing Ye, Haibo Hu

Erschienen in: Web Information Systems Engineering

Verlag: Springer Singapore

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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.

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Titel: Local Differential Privacy: Tools, Challenges, and Opportunities
verfasst von: Qingqing Ye
Haibo Hu
Verlag: Springer Singapore
Buch: Web Information Systems Engineering
Print ISBN: 978-981-15-3280-1

Electronic ISBN: 978-981-15-3281-8

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-981-15-3281-8_2

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