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Maintaining the Balance between Privacy and Data Integrity in Internet of Things

Published: 14 January 2017 Publication History

Abstract

The recent proliferation of human-carried mobile and smartphone devices has opened up opportunities of using crowd-sensing to collect sensory data in Internet of Things (IoT). As tapping into the sensory data and resources of the smartphones becomes common place, it is necessary to ensure the privacy of the device user while maintaining the accuracy and the integrity of the data collected. IoT system devices often sacrifice either user privacy or data integrity. It has also become important to limit the computational cost and burden on the user devices, as increasingly more services desire to tap into the resource that these devices provide. In this paper we propose a balanced truth discovery (BTD) framework that attempts to meet all three of the aforementioned needs: user privacy, data integrity, and limited computational cost. The BTD framework also reduces user participation in the truth discovery process. The nature of the BTD framework provides the possibility for easy modification (e.g. cryptography and weight assignment). This reduces computation cost for the user device, but also limits the interactions between the devices and the server, which is essential to data integrity. BTD framework also takes steps to blur the user device's original sensory data, by processing results in groups called zones. An enhanced method takes privacy preservation a step further, by protecting the user from an untrusted data-collecting party. Analysis of simulations running the framework provides evidence for the preservation of data integrity.

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  • (2022)Towards Personalized Privacy-Preserving Truth Discovery Over Crowdsourced Data StreamsIEEE/ACM Transactions on Networking10.1109/TNET.2021.311005230:1(327-340)Online publication date: Feb-2022
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cover image ACM Other conferences
ICMSS '17: Proceedings of the 2017 International Conference on Management Engineering, Software Engineering and Service Sciences
January 2017
339 pages
ISBN:9781450348348
DOI:10.1145/3034950
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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  • Wuhan Univ.: Wuhan University, China

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 January 2017

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Author Tags

  1. Crowd Sensing
  2. Data Integrity
  3. Internet of Things
  4. Privacy
  5. Smartphones
  6. Truth Discovery

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Cited By

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  • (2023)An Efficient and Lightweight Commitment Scheme for IoT Data Streams2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)10.1109/DCOSS-IoT58021.2023.00078(461-468)Online publication date: Jun-2023
  • (2022)A Fog-Centric Secure Cloud Storage SchemeIEEE Transactions on Sustainable Computing10.1109/TSUSC.2019.29149547:2(250-262)Online publication date: 1-Apr-2022
  • (2022)Towards Personalized Privacy-Preserving Truth Discovery Over Crowdsourced Data StreamsIEEE/ACM Transactions on Networking10.1109/TNET.2021.311005230:1(327-340)Online publication date: Feb-2022
  • (2022)IFMD: image fusion for malware detectionJournal of Computer Virology and Hacking Techniques10.1007/s11416-022-00445-y19:2(271-286)Online publication date: 16-Aug-2022
  • (2020)Cognitive and Scalable Technique for Securing IoT Networks Against Malware EpidemicsIEEE Access10.1109/ACCESS.2020.30119198(138508-138528)Online publication date: 2020
  • (2019)Forewarned is forearmedBenchmarking: An International Journal10.1108/BIJ-08-2018-0264ahead-of-print:ahead-of-printOnline publication date: 22-Jul-2019
  • (2019)Improvement of Malware Classification Using Hybrid Feature EngineeringSN Computer Science10.1007/s42979-019-0017-91:1Online publication date: 16-Aug-2019
  • (2018)A Three-Layer Privacy Preserving Cloud Storage Scheme Based on Computational Intelligence in Fog ComputingIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2017.27641092:1(3-12)Online publication date: Feb-2018
  • (2018)Provably Secure Identity-Based Signcryption Scheme for Crowdsourced Industrial Internet of Things EnvironmentsIEEE Internet of Things Journal10.1109/JIOT.2017.27415805:4(2904-2914)Online publication date: Aug-2018
  • (2018)Protected Bidding Against Compromised Information Injection in IoT-Based Smart GridSmart Grid and Internet of Things10.1007/978-3-030-05928-6_8(78-84)Online publication date: 30-Dec-2018
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