Context-Aware Local Information Privacy

Bo Jiang, Mohamed Seif, Ravi Tandon, Ming Li

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this paper, we study Local Information Privacy (LIP). As a context-aware privacy notion, LIP relaxes the de facto standard privacy notion of local differential privacy (LDP) by incorporating prior knowledge and therefore achieving better utility. We study the relationships between LIP and some of the representative privacy notions including LDP, mutual information and maximal leakage. We show that LIP provides strong instance-wise privacy protection compared to other context-aware privacy notions. Moreover, we present some useful properties of LIP, including post-processing, linkage, composability, transferability and robustness to imperfect prior knowledge. Then we study a general utility-privacy tradeoff framework, under which we derive LIP based privacy-preserving mechanisms for both discrete and continuous-valued data. Three types of perturbation mechanisms are studied in this paper: 1) randomized response (RR), 2) random sampling (RS) and 3) additive noise (AN) (e.g., Gaussian mechanism). Our privacy mechanisms incorporate the prior knowledge into the perturbation parameters so as to enhance utility. Finally, we present a comprehensive set of experiments on real datasets to illustrate the advantage of context-awareness and compare the utility-privacy tradeoffs provided by different mechanisms.

Original languageEnglish (US)
Article number9448019
Pages (from-to)3694-3708
Number of pages15
JournalIEEE Transactions on Information Forensics and Security
Volume16
DOIs
StatePublished - 2021

Keywords

  • Information-theoretic privacy
  • Local information privacy
  • Privacy-preserving data aggregation

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

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