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Privacy-preserving Crowd-sensed Trust Aggregation in the User-centeric Internet of People Networks

Published: 30 December 2020 Publication History

Abstract

Today we are relying on Internet technologies for numerous services, for example, personal communication, online businesses, recruitment, and entertainment. Over these networks, people usually create content, a skillful worker profile, and provide services that are normally watched and used by other users, thus developing a social network among people termed as the Internet of People. Malicious users could also utilize such platforms for spreading unwanted content that could bring catastrophic consequences to a social network provider and the society, if not identified on time. The use of trust management over these networks plays a vital role in the success of these services. Crowd-sensing people or network users for their views about certain content or content creators could be a potential solution to assess the trustworthiness of content creators and their content. However, the human involvement in crowd-sensing would have challenges of privacy preservation and preventing intentional assignment of the fake high score given to certain user/content. To address these challenges, in this article, we propose a novel trust model that evaluates the aggregate trustworthiness of the content creator and the content without compromising the privacy of the participating people in a crowdsource group. The proposed system has inherent properties of privacy protection of participants, performs operations in the decentralized setup, and considers the trust weights of participants in a private and secure way. The system ensures privacy of participants under the malicious and honest-but-curious adversarial models. We evaluated the performance of the system by developing a prototype and applying it to different real data from different online social networks.

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  • (2023)End-to-End Database Software SecuritySoftware10.3390/software20200072:2(163-176)Online publication date: 29-Mar-2023
  • (2022)Multi-Perspective Trust Management Framework for Crowdsourced IoT ServicesIEEE Transactions on Services Computing10.1109/TSC.2021.305221915:4(2396-2409)Online publication date: 1-Jul-2022
  • (2021)Trust-Related Attacks and Their Detection: A Trust Management Model for the Social IoTIEEE Transactions on Network and Service Management10.1109/TNSM.2020.304690618:3(3297-3308)Online publication date: Sep-2021

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Published In

cover image ACM Transactions on Cyber-Physical Systems
ACM Transactions on Cyber-Physical Systems  Volume 5, Issue 1
Special Issue on Security and Privacy for Connected CPS
January 2021
266 pages
ISSN:2378-962X
EISSN:2378-9638
DOI:10.1145/3446431
  • Editor:
  • Tei-Wei Kuo
Issue’s Table of Contents
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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Publication History

Published: 30 December 2020
Online AM: 07 May 2020
Accepted: 01 March 2020
Revised: 01 February 2020
Received: 01 July 2019
Published in TCPS Volume 5, Issue 1

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  1. Content rating
  2. crowdsourcing
  3. privacy-preserving system
  4. trustworthiness

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Cited By

View all
  • (2023)End-to-End Database Software SecuritySoftware10.3390/software20200072:2(163-176)Online publication date: 29-Mar-2023
  • (2022)Multi-Perspective Trust Management Framework for Crowdsourced IoT ServicesIEEE Transactions on Services Computing10.1109/TSC.2021.305221915:4(2396-2409)Online publication date: 1-Jul-2022
  • (2021)Trust-Related Attacks and Their Detection: A Trust Management Model for the Social IoTIEEE Transactions on Network and Service Management10.1109/TNSM.2020.304690618:3(3297-3308)Online publication date: Sep-2021

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