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On location privacy and quality of information in participatory sensing

Published: 24 October 2012 Publication History

Abstract

Participatory sensing applications typically bind sensor data to locations. Location privacy preserving mechanisms protecting the location of users introduce therefore an uncertainty in the collected data distributions. We consider two strategies to reconstruct a data distribution after k-anonymity has been applied to the users' location information. We investigate how different parameters for the location privacy preserving mechanism influence both the quality of information and the location privacy of the users. Our results show that the cloak area resulted from applying k-anonymity has a higher impact on both the quality of information and the location privacy than the number of users, k, that are cloaked together.

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    cover image ACM Conferences
    Q2SWinet '12: Proceedings of the 8h ACM symposium on QoS and security for wireless and mobile networks
    October 2012
    98 pages
    ISBN:9781450316194
    DOI:10.1145/2387218
    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: 24 October 2012

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    Author Tags

    1. data collection
    2. location privacy
    3. participatory sensing
    4. quality of information

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    Overall Acceptance Rate 46 of 131 submissions, 35%

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    • (2020)Tradeoff Between Location Quality and Privacy in Crowdsensing: An Optimization PerspectiveIEEE Internet of Things Journal10.1109/JIOT.2020.29725557:4(3535-3544)Online publication date: Apr-2020
    • (2019)Optimizing Location Quality in Privacy Preserving Crowdsensing2019 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOBECOM38437.2019.9014089(1-6)Online publication date: Dec-2019
    • (2019)Localizing Backscatters by a Single Robot with Zero Start-Up Cost2019 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOBECOM38437.2019.9013768(1-6)Online publication date: Dec-2019
    • (2016)OP4: An OPPortunistic Privacy-Preserving Scheme for Crowdsensing Applications2016 IEEE 41st Conference on Local Computer Networks (LCN)10.1109/LCN.2016.75(460-468)Online publication date: Nov-2016
    • (2016)Privacy, Trust and Incentives in Participatory SensingParticipatory Sensing, Opinions and Collective Awareness10.1007/978-3-319-25658-0_5(93-114)Online publication date: 14-May-2016
    • (2015)A distributed privacy-preserving mechanism for mobile urban sensing applications2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)10.1109/ISSNIP.2015.7106932(1-6)Online publication date: Apr-2015

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