TY - JOUR
T1 - Context-Aware Local Information Privacy
AU - Jiang, Bo
AU - Seif, Mohamed
AU - Tandon, Ravi
AU - Li, Ming
N1 - Funding Information:
Manuscript received December 4, 2020; revised March 7, 2021; accepted May 10, 2021. Date of publication June 7, 2021; date of current version July 28, 2021. This work was supported by NSF under Grant CNS-1715947, Grant CAREER-1651492, Grant CCF-2100013, and Grant CNS-1731164. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Anna Squicciarini. (Bo Jiang and Mohamed Seif are co-first authors.) (Corresponding author: Bo Jiang.) The authors are with the Department of Electrical and Computer Engineering, The University of Arizona, AZ 85718 USA (e-mail: bjiang@ email.arizona.edu; mseif@email.arizona.edu; tandonr@email.arizona.edu; lim@email.arizona.edu).
Publisher Copyright:
© 2005-2012 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Information-theoretic privacy
KW - Local information privacy
KW - Privacy-preserving data aggregation
UR - http://www.scopus.com/inward/record.url?scp=85111028896&partnerID=8YFLogxK
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U2 - 10.1109/TIFS.2021.3087350
DO - 10.1109/TIFS.2021.3087350
M3 - Article
AN - SCOPUS:85111028896
SN - 1556-6013
VL - 16
SP - 3694
EP - 3708
JO - IEEE Transactions on Information Forensics and Security
JF - IEEE Transactions on Information Forensics and Security
M1 - 9448019
ER -