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Privacy-Preserving Truth Discovery in Crowd Sensing Systems

Published: 09 January 2019 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 better utilize such sensory data, the topic of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has drawn significant attention. Though able to improve aggregation accuracy, existing truth discovery approaches fail to address the privacy concerns of individual users. In this article, we propose a novel privacy-preserving truth discovery (PPTD) framework, which can protect 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 a homomorphic cryptosystem, which can guarantee both high accuracy and strong privacy protection. In order to deal with large-scale data, we also propose to parallelize PPTD with MapReduce framework. Additionally, we design an incremental PPTD scheme for the scenarios where the sensory data are collected in a streaming manner. Extensive experiments based on two real-world crowd sensing systems demonstrate that the proposed framework can generate accurate aggregated results while protecting users’ private information.

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cover image ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks  Volume 15, Issue 1
February 2019
382 pages
ISSN:1550-4859
EISSN:1550-4867
DOI:10.1145/3300201
Issue’s Table of Contents
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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Publication History

Published: 09 January 2019
Accepted: 01 September 2018
Revised: 01 April 2018
Received: 01 September 2017
Published in TOSN Volume 15, Issue 1

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  1. Crowd sensing
  2. privacy-preserving<?pgbrk?>
  3. truth discovery

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  • (2025)Mobile crowdsourcing based on 5G and 6G: A surveyNeurocomputing10.1016/j.neucom.2024.128993618(128993)Online publication date: Feb-2025
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