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SHIELD: a data verification framework for participatory sensing systems

Published: 22 June 2015 Publication History

Abstract

The openness of PS systems renders them vulnerable to malicious users that can pollute the measurement collection process, in an attempt to degrade the PS system data and, overall, its usefulness. Mitigating such adversarial behavior is hard. Cryptographic protection, authentication, authorization, and access control can help but they do not fully address the problem. Reports from faulty insiders (participants with credentials) can target the process intelligently, forcing the PS system to deviate from the actual sensed phenomenon. Filtering out those faulty reports is challenging, with practically no prior knowledge on the participants' trustworthiness, dynamically changing phenomena, and possibly large numbers of compromised devices. This paper proposes SHIELD, a novel data verification framework for PS systems that can complement any security architecture. SHIELD handles available, contradicting evidence, classifies efficiently incoming reports, and effectively separates and rejects those that are faulty. As a result, the deemed correct data can accurately represent the sensed phenomena, even when 45% of the reports are faulty, intelligently selected by coordinated adversaries and targeted optimally across the system's coverage area.

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cover image ACM Conferences
WiSec '15: Proceedings of the 8th ACM Conference on Security & Privacy in Wireless and Mobile Networks
June 2015
256 pages
ISBN:9781450336239
DOI:10.1145/2766498
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: 22 June 2015

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Author Tags

  1. participatory sensing
  2. privacy
  3. security

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WiSec'15
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  • US Army Research Office
  • NSF

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Overall Acceptance Rate 98 of 338 submissions, 29%

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  • (2024)Security and Privacy for Mobile Crowdsensing: Improving User Relevance and PrivacyComputer Security. ESORICS 2023 International Workshops10.1007/978-3-031-54204-6_28(474-493)Online publication date: 1-Mar-2024
  • (2023)Dynamic Watermarking for Cybersecurity of Autonomous VehiclesIEEE Transactions on Industrial Electronics10.1109/TIE.2022.322933370:11(11735-11743)Online publication date: Nov-2023
  • (2023)Security and Privacy Challenges of Participatory Sensing in Natural Disaster Management2023 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA)10.1109/AICCSA59173.2023.10479256(1-6)Online publication date: 4-Dec-2023
  • (2022)Sybil-Based Attacks on Google Maps or How to Forge the Image of City LifeProceedings of the 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks10.1145/3507657.3528538(73-84)Online publication date: 16-May-2022
  • (2022)Participatory Sensing for Localization of a GNSS Jammer2022 International Conference on Localization and GNSS (ICL-GNSS)10.1109/ICL-GNSS54081.2022.9797031(1-7)Online publication date: 7-Jun-2022
  • (2021)Recurring verification of interaction authenticity within bluetooth networksProceedings of the 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks10.1145/3448300.3468287(192-203)Online publication date: 28-Jun-2021
  • (2020)Understanding the Potential of Edge-Based Participatory Sensing: an Experimental Study2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)10.1109/VTC2020-Spring48590.2020.9129084(1-5)Online publication date: May-2020
  • (2019)Privacy Aware Incentivization for Participatory SensingSensors10.3390/s1918404919:18(4049)Online publication date: 19-Sep-2019
  • (2019)Participatory location fingerprinting through stationary crowd in a public or commercial indoor environmentProceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3360774.3360791(424-433)Online publication date: 12-Nov-2019
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