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An Efficient Two-Layer Mechanism for Privacy-Preserving Truth Discovery

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Kui RenAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 1705 - 1714

https://doi.org/10.1145/3219819.3219998

Published: 19 July 2018 Publication History

Abstract

Soliciting answers from online users is an efficient and effective solution to many challenging tasks. Due to the variety in the quality of users, it is important to infer their ability to provide correct answers during aggregation. Therefore, truth discovery methods can be used to automatically capture the user quality and aggregate user-contributed answers via a weighted combination. Despite the fact that truth discovery is an effective tool for answer aggregation, existing work falls short of the protection towards the privacy of participating users. To fill this gap, we propose perturbation-based mechanisms that provide users with privacy guarantees and maintain the accuracy of aggregated answers. We first present a one-layer mechanism, in which all the users adopt the same probability to perturb their answers. Aggregation is then conducted on perturbed answers but the aggregation accuracy could drop accordingly. To improve the utility, a two-layer mechanism is proposed where users are allowed to sample their own probabilities from a hyper distribution. We theoretically compare the one-layer and two-layer mechanisms, and prove that they provide the same privacy guarantee while the two-layer mechanism delivers better utility. This advantage is brought by the fact that the two-layer mechanism can utilize the estimated user quality information from truth discovery to reduce the accuracy loss caused by perturbation, which is confirmed by experimental results on real-world datasets. Experimental results also demonstrate the effectiveness of the proposed two-layer mechanism in privacy protection with tolerable accuracy loss in aggregation.

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Cited By

Peng TZhong WWang GLuo EYu SLiu YYang YZhang X(2024)Privacy-Preserving Truth Discovery Based on Secure Multi-Party Computation in Vehicle-Based Mobile CrowdsensingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.335020825:7(7767-7779)Online publication date: Jul-2024
https://doi.org/10.1109/TITS.2024.3350208
Zheng ZLi ZHuang CLong SLi MShen X(2024)Data Poisoning Attacks and Defenses to LDP-based Privacy-Preserving CrowdsensingIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.3363507(1-18)Online publication date: 2024
https://doi.org/10.1109/TDSC.2024.3363507
Zhang PCheng XSu SZhu B(2023)PrivTDSI: A Local Differentially Private Approach for Truth Discovery via Sampling and InferenceIEEE Transactions on Big Data10.1109/TBDATA.2022.31861759:2(471-484)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TBDATA.2022.3186175
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Index Terms

An Efficient Two-Layer Mechanism for Privacy-Preserving Truth Discovery
1. Information systems
  1. Information systems applications
    1. Data mining
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections

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KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 19 July 2018

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KDD '18

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KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

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KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Peng TZhong WWang GLuo EYu SLiu YYang YZhang X(2024)Privacy-Preserving Truth Discovery Based on Secure Multi-Party Computation in Vehicle-Based Mobile CrowdsensingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.335020825:7(7767-7779)Online publication date: Jul-2024
https://doi.org/10.1109/TITS.2024.3350208
Zheng ZLi ZHuang CLong SLi MShen X(2024)Data Poisoning Attacks and Defenses to LDP-based Privacy-Preserving CrowdsensingIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.3363507(1-18)Online publication date: 2024
https://doi.org/10.1109/TDSC.2024.3363507
Zhang PCheng XSu SZhu B(2023)PrivTDSI: A Local Differentially Private Approach for Truth Discovery via Sampling and InferenceIEEE Transactions on Big Data10.1109/TBDATA.2022.31861759:2(471-484)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TBDATA.2022.3186175
Mukkamala PWu HDüdder B(2023)Reliable and Streaming Truth Discovery in Blockchain-based Crowdsourcing2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)10.1109/SECON58729.2023.10287465(492-500)Online publication date: 11-Sep-2023
https://doi.org/10.1109/SECON58729.2023.10287465
Xiong PLi GLiu HHu YJin D(2023)Decentralized privacy-preserving truth discovery for crowd sensingInformation Sciences: an International Journal10.1016/j.ins.2023.03.046632:C(730-741)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.ins.2023.03.046
Wang ZCheng XSu SWang L(2022)Achieving Private and Fair Truth Discovery in Crowdsourcing SystemsSecurity and Communication Networks10.1155/2022/92817292022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9281729
Wu HWang LCheng KYang DTang JXue G(2022)Privacy-Enhanced and Practical Truth Discovery in Two-Server Mobile CrowdsensingIEEE Transactions on Network Science and Engineering10.1109/TNSE.2022.31512289:3(1740-1755)Online publication date: 1-May-2022
https://doi.org/10.1109/TNSE.2022.3151228
Pang XWang ZLiu DLui JWang QRen J(2022)Towards Personalized Privacy-Preserving Truth Discovery Over Crowdsourced Data StreamsIEEE/ACM Transactions on Networking10.1109/TNET.2021.311005230:1(327-340)Online publication date: Feb-2022
https://doi.org/10.1109/TNET.2021.3110052
Li ZZheng ZGuo SGuo BXiao FRen K(2022)Disguised as Privacy: Data Poisoning Attacks against Differentially Private Crowdsensing SystemsIEEE Transactions on Mobile Computing10.1109/TMC.2022.3173642(1-1)Online publication date: 2022
https://doi.org/10.1109/TMC.2022.3173642
Wang DRen JWang ZPang XZhang YShen X(2022)Privacy-Preserving Streaming Truth Discovery in Crowdsourcing With Differential PrivacyIEEE Transactions on Mobile Computing10.1109/TMC.2021.306277521:10(3757-3772)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TMC.2021.3062775
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