research-article

Location Privacy-Preserving Data Recovery for Mobile Crowdsensing

Authors:

Yueyue ChenAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 3

Article No.: 151, Pages 1 - 23

https://doi.org/10.1145/3264961

Published: 18 September 2018 Publication History

Abstract

Data recovery techniques such as compressive sensing are commonly used in mobile crowdsensing (MCS) applications to infer the information of unsensed regions based on data from nearby participants. However, the participants' locations are exposed when they report geo-tagged data to an application server. While there are considerable location protection approaches for MCS, they fail to maintain the correlation of sensory data, leading to the existence of unrecoverable data. None of the previous approaches can achieve both data recovery and data privacy preservation. We propose a novel location privacy-preserving data recovery method in this paper. Based on our discovery that the adjacency relations of non-zero elements are key to the missing data recovery in a crowdsensing data matrix, we design a correlation-preserving location obfuscation scheme to hide the participants' locations under effective camouflage. We also design an encrypted data recovery scheme based on the homomorphic encryption in order to avoid location privacy leakage from sensory data. Location obfuscation and data encryption preserve the participants' privacy, while the correlation-preserving and homomorphic properties of our method ensure data recovery accuracy. Evaluations of real-world datasets show that our privacy-preserving method can effectively obfuscate locations (e.g., yielding an average location distortion of 1.7km in a 2.4km x 4km area for successful location hiding), and it can efficiently achieve similar data recovery accuracy to compressive sensing (which has no privacy protection).

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Cited By

Zheng YZhou MWang SHuang HJia XYi XWang C(2024)SecDR: Enabling Secure, Efficient, and Accurate Data Recovery for Mobile CrowdsensingIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.326226821:2(789-803)Online publication date: Mar-2024
https://doi.org/10.1109/TDSC.2023.3262268
Alkanjr BMahgoub I(2023)Location Privacy-Preserving Scheme in IoBT Networks Using Deception-Based TechniquesSensors10.3390/s2306314223:6(3142)Online publication date: 15-Mar-2023
https://doi.org/10.3390/s23063142
Wang WWang YDuan PLiu TTong XCai Z(2023)A Triple Real-Time Trajectory Privacy Protection Mechanism Based on Edge Computing and Blockchain in Mobile CrowdsourcingIEEE Transactions on Mobile Computing10.1109/TMC.2022.318704722:10(5625-5642)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TMC.2022.3187047
Show More Cited By

Index Terms

Location Privacy-Preserving Data Recovery for Mobile Crowdsensing
1. Human-centered computing
  1. Ubiquitous and mobile computing
2. Networks
  1. Network properties
    1. Network privacy and anonymity
  2. Network services
    1. Cloud computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 2, Issue 3

September 2018

1536 pages

EISSN:2474-9567

DOI:10.1145/3279953

Issue’s Table of Contents

Copyright © 2018 ACM.

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Publication History

Published: 18 September 2018

Accepted: 01 September 2018

Revised: 01 April 2018

Received: 01 February 2018

Published in IMWUT Volume 2, Issue 3

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National Key Research and Development Program of China
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Cited By

Zheng YZhou MWang SHuang HJia XYi XWang C(2024)SecDR: Enabling Secure, Efficient, and Accurate Data Recovery for Mobile CrowdsensingIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.326226821:2(789-803)Online publication date: Mar-2024
https://doi.org/10.1109/TDSC.2023.3262268
Alkanjr BMahgoub I(2023)Location Privacy-Preserving Scheme in IoBT Networks Using Deception-Based TechniquesSensors10.3390/s2306314223:6(3142)Online publication date: 15-Mar-2023
https://doi.org/10.3390/s23063142
Wang WWang YDuan PLiu TTong XCai Z(2023)A Triple Real-Time Trajectory Privacy Protection Mechanism Based on Edge Computing and Blockchain in Mobile CrowdsourcingIEEE Transactions on Mobile Computing10.1109/TMC.2022.318704722:10(5625-5642)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TMC.2022.3187047
Gao LYao ZGao L(2023)Research on Cost Control of Mobile Crowdsourcing Supporting Low Budget in Large Scale Environmental Information MonitoringComputer Supported Cooperative Work and Social Computing10.1007/978-981-99-2385-4_11(148-163)Online publication date: 13-May-2023
https://doi.org/10.1007/978-981-99-2385-4_11
Guo BLiu YLiu SYu ZZhou X(2022)CrowdHMT: Crowd Intelligence With the Deep Fusion of Human, Machine, and IoTIEEE Internet of Things Journal10.1109/JIOT.2022.31947269:24(24822-24842)Online publication date: 15-Dec-2022
https://doi.org/10.1109/JIOT.2022.3194726
Zhou TCai ZSu J(2022)Discovering Truth in Mobile Crowdsensing with Differential Location PrivacyGLOBECOM 2022 - 2022 IEEE Global Communications Conference10.1109/GLOBECOM48099.2022.10001292(903-908)Online publication date: 4-Dec-2022
https://doi.org/10.1109/GLOBECOM48099.2022.10001292
Wang WWang YHuang YMu CSun ZTong XCai Z(2022)Privacy protection federated learning system based on blockchain and edge computing in mobile crowdsourcingComputer Networks10.1016/j.comnet.2022.109206215(109206)Online publication date: Oct-2022
https://doi.org/10.1016/j.comnet.2022.109206
Zhou TCai ZLiu F(2021)The Crowd Wisdom for Location Privacy of Crowdsensing PhotosProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34781065:3(1-23)Online publication date: 14-Sep-2021
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Liu JWang L(2021)A Mobile Data Recovery Device2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems10.1007/978-981-16-1726-3_99(807-812)Online publication date: 2-Jun-2021
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Sun ZWang YCai ZLiu TTong XJiang N(2021)A two‐stage privacy protection mechanism based on blockchain in mobile crowdsourcingInternational Journal of Intelligent Systems10.1002/int.2237136:5(2058-2080)Online publication date: 31-Mar-2021
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