research-article

Cloud-Enabled Privacy-Preserving Truth Discovery in Crowd Sensing Systems

Authors:

Kui RenAuthors Info & Claims

SenSys '15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

Pages 183 - 196

https://doi.org/10.1145/2809695.2809719

Published: 01 November 2015 Publication History

Abstract

The recent proliferation of human-carried mobile devices has given rise to the crowd sensing systems. However, the sensory data provided by individual participants are usually not reliable. To identify truthful values from the crowd sensing data, the topic of truth discovery, whose goal is to estimate user quality and infer truths through quality-aware data aggregation, has drawn significant attention. Though able to improve aggregation accuracy, existing truth discovery approaches fail to take into consideration an important issue in their design, i.e., the protection of individual users' private information. In this paper, we propose a novel cloud-enabled privacy-preserving truth discovery (PPTD) framework for crowd sensing systems, which can achieve the protection of not only users' sensory data but also their reliability scores derived by the truth discovery approaches. The key idea of the proposed framework is to perform weighted aggregation on users' encrypted data using homomorphic cryptosystem. In order to deal with large-scale data, we also propose to parallelize PPTD with MapReduce framework. Through extensive experiments on not only synthetic data but also real world crowd sensing systems, we justify the guarantee of strong privacy and high accuracy of our proposed framework.

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Index Terms

Cloud-Enabled Privacy-Preserving Truth Discovery in Crowd Sensing Systems

Recommendations

Truth Discovery on Crowd Sensing of Correlated Entities
SenSys '15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

With the popular usage of mobile devices and smartphones, crowd sensing becomes pervasive in real life when human acts as sensors to report their observations about entities. For the same entity, users may report conflicting information, and thus it is ...
Privacy-Preserving Truth Discovery in Crowd Sensing Systems

The recent proliferation of human-carried mobile devices has given rise to the crowd sensing systems. However, the sensory data provided by individual participants are usually not reliable. To better utilize such sensory data, the topic of truth ...
Decentralized privacy-preserving truth discovery for crowd sensing
Abstract
Truth discovery is an efficient technique for tackling data conflict problems in crowd sensing for distributed data collection. As the sensory data to be collected may include sensitive information about users, privacy-preserving truth ...
Highlights
- DPriTD is a decentralized privacy-preserving framework for truth discovery in crowd sensing.

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Published In

cover image ACM Conferences

SenSys '15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

November 2015

526 pages

ISBN:9781450336314

DOI:10.1145/2809695

General Chair:
Junehwa Song
KAIST, South Korea
,
Program Chairs:
Tarek Abdelzaher
University of Illinois at Urbana-Champaign, USA
,
Cecilia Mascolo
University of Cambridge, UK

Copyright © 2015 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: 01 November 2015

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SenSys '15

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SenSys '15: The 13th ACM Conference on Embedded Network Sensor Systems

November 1 - 4, 2015

Seoul, South Korea

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SenSys '15 Paper Acceptance Rate 27 of 132 submissions, 20%;

Overall Acceptance Rate 174 of 867 submissions, 20%

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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
Shen XXu CZhu LLu RGuan YZhang X(2024)Blockchain-Based Lightweight and Privacy-Preserving Quality Assurance Framework in Crowdsensing SystemsIEEE Internet of Things Journal10.1109/JIOT.2023.328834911:1(974-986)Online publication date: 1-Jan-2024
https://doi.org/10.1109/JIOT.2023.3288349
Wang TXu NZhang QChen FXie DZhao C(2024)A lightweight privacy-preserving truth discovery in mobile crowdsensing systemsJournal of Information Security and Applications10.1016/j.jisa.2024.10379283(103792)Online publication date: Jun-2024
https://doi.org/10.1016/j.jisa.2024.103792
Wu XSong YLai J(2024)A multi-dimensional framework for improving data reliability in mobile crowd sensingEgyptian Informatics Journal10.1016/j.eij.2024.10051827(100518)Online publication date: Sep-2024
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Xu HQi SQi YWei WXiong N(2023)Secure and Lightweight Blockchain-based Truthful Data Trading for Real-Time Vehicular CrowdsensingACM Transactions on Embedded Computing Systems10.1145/358200823:1(1-31)Online publication date: 25-Jan-2023
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Cheng YMa JLiu ZLi ZWu YDong CLi R(2023)A Privacy-Preserving and Reputation-Based Truth Discovery Framework in Mobile CrowdsensingIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.327697620:6(5293-5311)Online publication date: Nov-2023
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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
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