本地化差分隐私研究综述

Translated title of the contribution: Survey on Local Differential Privacy

Qing Qing Ye, Xiao Feng Meng, Min Jie Zhu, Zheng Huo

Research output: Journal article publicationReview articleAcademic researchpeer-review

16 Citations (Scopus)

Abstract

With the development of information technology in the big data era, there has been a growing concern for privacy of personal information. Privacy preserving is a key challenge when releasing and analyzing data. Centralized differential privacy is based on the assumption of a trustworthy data collector; however, it is actually a bit difficult to realize in practice. To address this issue, local differential privacy has emerged as a new model for privacy preserving with strong privacy guarantees. By resisting adversaries with any background knowledge and preventing attacks from untrustworthy data collector, local differential privacy can protect private information thoroughly. Starting with an introduction to the mechanisms and properties, this paper surveys the state of the art of local differential privacy, focusing on the frequency estimation, mean value estimation and the design of perturbation model. Following a comprehensive comparison and analysis of existing techniques, further research challenges are put forward.

Translated title of the contributionSurvey on Local Differential Privacy
Original languageChinese
Pages (from-to)1981-2005
Number of pages25
JournalRuan Jian Xue Bao/Journal of Software
Volume29
Issue number7
DOIs
Publication statusPublished - 1 Jul 2018
Externally publishedYes

Keywords

  • Centralized differential privacy
  • Local differential privacy
  • Privacy preserving

ASJC Scopus subject areas

  • Software

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