Local Differential Privacy: Tools, Challenges, and Opportunities

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering - WISE 2019 Workshop, Demo, and Tutorial, Revised Selected Papers
EditorsLeong Hou U, Jian Yang, Yi Cai, Kamalakar Karlapalem, An Liu, Xin Huang
PublisherSpringer
Pages13-23
Number of pages11
ISBN (Print)9789811532801
DOIs
Publication statusPublished - 19 Jan 2020
Event20th International Conference on Web Information Systems Engineering, WISE 2019 and on the International Workshop on Web Information Systems in the Era of AI, 2019 - Hong Kong, China
Duration: 19 Jan 202022 Jan 2020

Publication series

NameCommunications in Computer and Information Science
Volume1155 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference20th International Conference on Web Information Systems Engineering, WISE 2019 and on the International Workshop on Web Information Systems in the Era of AI, 2019
CountryChina
CityHong Kong
Period19/01/2022/01/20

Keywords

  • Data analysis
  • Data collection
  • Local differential privacy

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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