research-article

Prospect Theoretic Analysis of Privacy-Preserving Mechanism

Authors:

Jianwei HuangAuthors Info & Claims

IEEE/ACM Transactions on Networking, Volume 28, Issue 1

Pages 71 - 83

https://doi.org/10.1109/TNET.2019.2951713

Published: 14 February 2020 Publication History

Abstract

We study a problem of privacy-preserving mechanism design. A data collector wants to obtain data from individuals to perform some computations. To relieve the privacy threat to the contributors, the data collector adopts a privacy-preserving mechanism by adding random noise to the computation result, at the cost of reduced accuracy. Individuals decide whether to contribute data when faced with the privacy issue. Due to the intrinsic uncertainty in privacy protection, we model individuals’ privacy-related decision using Prospect Theory. Such a theory more accurately models individuals’ behavior under uncertainty than the traditional expected utility theory, whose prediction always deviates from practical human behavior. We show that the data collector’s utility maximization problem involves a polynomial of high and fractional order, the root of which is difficult to compute analytically. We get around this issue by considering a large population approximation, and obtain a closed-form solution that well approximates the precise solution. We discover that the data collector who considers the more realistic Prospect Theory based individual decision modeling would adopt a more conservative privacy-preserving mechanism, compared with the case based on the expected utility theory modeling. We also study the impact of Prospect Theory parameters, and concludes that more loss-averse or risk-seeking individuals will trigger a more conservative mechanism. When individuals have different Prospect Theory parameters, simulations demonstrate that the privacy protection first becomes stronger and then becomes weaker as the heterogeneity increases from a low value to a high one.

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cover image IEEE/ACM Transactions on Networking

IEEE/ACM Transactions on Networking Volume 28, Issue 1

Feb. 2020

426 pages

ISSN:1063-6692

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1063-6692 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 14 February 2020

Published in TON Volume 28, Issue 1

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Cited By

Yan HTang X(2023)Incorporating Range Anxiety into Electric Vehicle Highway Charging Decisions: A Bayesian Game Analysis✱Proceedings of the 14th ACM International Conference on Future Energy Systems10.1145/3575813.3595195(184-188)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3575813.3595195
Ma EBirrell E(2022)Prospective Consent: The Effect of Framing on Cookie Consent DecisionsCHI Conference on Human Factors in Computing Systems Extended Abstracts10.1145/3491101.3519687(1-6)Online publication date: 27-Apr-2022
https://dl.acm.org/doi/10.1145/3491101.3519687
Chen YZhao QDuan PZhang BHong ZWang B(2022)Verifiable privacy-preserving association rule mining using distributed decryption mechanism on the cloudExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117086201:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.117086
Zhang LZhu TXiong PZhou WYu P(2021)More than PrivacyACM Computing Surveys10.1145/346077154:7(1-37)Online publication date: 18-Jul-2021
https://dl.acm.org/doi/10.1145/3460771

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