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Techu: Open and Privacy-Preserving Crowdsourced GPS for the Masses

Authors:

Ioannis Agadakos,

Georgios PortokalidisAuthors Info & Claims

MobiSys '17: Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services

Pages 475 - 487

https://doi.org/10.1145/3081333.3081345

Published: 16 June 2017 Publication History

Abstract

The proliferation of mobile devices, equipped with numerous sensors and Internet connectivity, has laid the foundation for the emergence of a diverse set of crowdsourcing services. By leveraging the multitude, geographical dispersion, and technical abilities of smartphones, these services tackle challenging tasks by harnessing the power of the crowd. One such service, Crowd GPS, has gained traction in the industry and research community alike, materializing as a class of systems that track lost objects or individuals (e.g., children or elders). While these systems can have significant impact, they suffer from major privacy threats.

In this paper, we highlight the inherent risks to users from the centralized designs adopted by such services and demonstrate how adversaries can trivially misuse one of the most popular crowd GPS services to track their users. As an alternative, we present Techu, a privacy-preserving crowd GPS service for tracking Bluetooth tags. Our architecture follows a hybrid decentralized approach, where an untrusted server acts as a bulletin board that collects reports of tags observed by the crowd, while observers store the location information locally and only disclose it upon proof of ownership of the tag. Techu does not require user authentication, allowing users to remain anonymous. As no user authentication is required and cloud messaging queues are leveraged for communication between users, users remain anonymous. Our security analysis highlights the privacy offered by Techu, and details how our design prevents adversaries from tracking or identifying users. Finally, our experimental evaluation demonstrates that Techu has negligible impact on power consumption, and achieves superior effectiveness to previously proposed systems while offering stronger privacy guarantees.

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Cited By

Torre DChennamaneni ARodriguez A(2023)Privacy-Preservation Techniques for IoT Devices: A Systematic Mapping StudyIEEE Access10.1109/ACCESS.2023.324552411(16323-16345)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3245524
Wang YTian YZhang XHe XLi SZhu J(2022)Deep Reinforcement Learning Based Iterative Participant Selection Method for Industrial IoT Big Data Mobile CrowdsourcingAdvanced Data Mining and Applications10.1007/978-3-030-95405-5_19(258-272)Online publication date: 31-Jan-2022
https://doi.org/10.1007/978-3-030-95405-5_19
Garg CMachiry AContinella AKruegel CVigna GPöpper CVanhoef MBatina LMayrhofer R(2021)Toward a secure crowdsourced location tracking systemProceedings of the 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks10.1145/3448300.3467821(311-322)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1145/3448300.3467821
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Index Terms

Techu: Open and Privacy-Preserving Crowdsourced GPS for the Masses
1. Networks
  1. Network services
    1. Location based services
2. Security and privacy

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cover image ACM Conferences

MobiSys '17: Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services

June 2017

520 pages

ISBN:9781450349284

DOI:10.1145/3081333

General Chairs:
Tanzeem Choudhury
Cornell University, USA
,
Steve Ko
University at Buffalo, USA
,
Program Chairs:
Andrew Campbell
Dartmouth College, USA
,
Deepak Ganesan
University of Massachusetts, USA

Copyright © 2017 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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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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SIGOPS: ACM Special Interest Group on Operating Systems

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Association for Computing Machinery

New York, NY, United States

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Published: 16 June 2017

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Office of Naval Research

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MobiSys'17

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SIGMOBILE

MobiSys'17: The 15th Annual International Conference on Mobile Systems, Applications, and Services

June 19 - 23, 2017

New York, Niagara Falls, USA

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MobiSys '17 Paper Acceptance Rate 34 of 188 submissions, 18%;

Overall Acceptance Rate 274 of 1,679 submissions, 16%

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Cited By

Torre DChennamaneni ARodriguez A(2023)Privacy-Preservation Techniques for IoT Devices: A Systematic Mapping StudyIEEE Access10.1109/ACCESS.2023.324552411(16323-16345)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3245524
Wang YTian YZhang XHe XLi SZhu J(2022)Deep Reinforcement Learning Based Iterative Participant Selection Method for Industrial IoT Big Data Mobile CrowdsourcingAdvanced Data Mining and Applications10.1007/978-3-030-95405-5_19(258-272)Online publication date: 31-Jan-2022
https://doi.org/10.1007/978-3-030-95405-5_19
Garg CMachiry AContinella AKruegel CVigna GPöpper CVanhoef MBatina LMayrhofer R(2021)Toward a secure crowdsourced location tracking systemProceedings of the 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks10.1145/3448300.3467821(311-322)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1145/3448300.3467821
Sun YLiu MHuang LXie NZhao LTan W(2021)An Embedding-based Deterministic Policy Gradient Model for Spatial Crowdsourcing Applications2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD49262.2021.9437770(1268-1274)Online publication date: 5-May-2021
https://doi.org/10.1109/CSCWD49262.2021.9437770
Shen WHe XZhang CNi QDou WWang Yd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile CrowdsourcingProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3411913(1355-1364)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3411913
Tsubouchi KTeraoka TGomi HShimosaka M(2020)Parasitic Location Logging: Estimating Users’ Location from Context of Passersby2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom45495.2020.9127381(1-10)Online publication date: Mar-2020
https://doi.org/10.1109/PerCom45495.2020.9127381
Li YXiao HQin ZMiao CSu LGao JRen KDing B(2020)Towards Differentially Private Truth Discovery for Crowd Sensing Systems2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS47774.2020.00037(1156-1166)Online publication date: Nov-2020
https://doi.org/10.1109/ICDCS47774.2020.00037
Jo HChoi W(2019)BPRF: Blockchain-based privacy-preserving reputation framework for participatory sensing systemsPLOS ONE10.1371/journal.pone.022568814:12(e0225688)Online publication date: 5-Dec-2019
https://doi.org/10.1371/journal.pone.0225688
Chen KTan G(2019)BikeGPSACM Transactions on Sensor Networks10.1145/334385715:4(1-28)Online publication date: 17-Oct-2019
https://dl.acm.org/doi/10.1145/3343857
Haus MDing AOtt J(2019)LocalVLC: Augmenting Smart IoT Services with Practical Visible Light Communication2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)10.1109/WoWMoM.2019.8793022(1-9)Online publication date: Jun-2019
https://doi.org/10.1109/WoWMoM.2019.8793022
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