ORCID Profile
0000-0002-4918-2744
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Publisher: IEEE
Date: 11-2022
Publisher: IEEE
Date: 12-07-2021
Publisher: MDPI AG
Date: 18-04-2023
DOI: 10.3390/E25040679
Abstract: This paper investigates lift, the likelihood ratio between the posterior and prior belief about sensitive features in a dataset. Maximum and minimum lifts over sensitive features quantify the adversary’s knowledge gain and should be bounded to protect privacy. We demonstrate that max- and min-lifts have a distinct range of values and probability of appearance in the dataset, referred to as lift asymmetry. We propose asymmetric local information privacy (ALIP) as a compatible privacy notion with lift asymmetry, where different bounds can be applied to min- and max-lifts. We use ALIP in the watchdog and optimal random response (ORR) mechanisms, the main methods to achieve lift-based privacy. It is shown that ALIP enhances utility in these methods compared to existing local information privacy, which ensures the same (symmetric) bounds on both max- and min-lifts. We propose subset merging for the watchdog mechanism to improve data utility and subset random response for the ORR to reduce complexity. We then investigate the related lift-based measures, including ℓ1-norm, χ2-privacy criterion, and α-lift. We reveal that they can only restrict max-lift, resulting in significant min-lift leakage. To overcome this problem, we propose corresponding lift-inverse measures to restrict the min-lift. We apply these lift-based and lift-inverse measures in the watchdog mechanism. We show that they can be considered as relaxations of ALIP, where a higher utility can be achieved by bounding only average max- and min-lifts.
Publisher: IEEE
Date: 23-05-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
Location: Iran (Islamic Republic of)
No related grants have been discovered for Mohammad Amin Zarrabian.