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    Privacy-preserving crowd-sensed trust aggregation in the user-centeric internet of people networks

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    Authors
    Azad, Muhammad
    Perera, Charith
    Bag, Samiran
    Barhamgi, Mahmoud
    Hao, Feng
    Affiliation
    University of Derby
    Cardiff University
    University of Warwick
    Universite Claude Bernard Lyon
    Issue Date
    2020
    
    Metadata
    Show full item record
    Abstract
    Today we are relying on the Internet technologies for various types of services ranging from personal communication to the entertainment. The online social networks (Facebook, twitter, youtube) has seen an increase in subscribers in recent years developing a social network among people termed as the Internet of People. In such a network, subscribers use the content disseminated by other subscribers. The malicious users can also utilize such platforms for spreading the malicious and fake content that would bring catastrophic consequences to a social network if not identified on time. People crowd-sensing on the Internet of people system has seen a prospective solution for the large scale data collection by leveraging the feedback collections from the people of the internet that would not only help in identifying malicious subscribers of the network but would also help in defining better services. However, the human involvement in crowd-sensing would have challenges of privacy-preservation, intentional spread of false high score about certain user/content undermining the services, and assigning different trust scores to the peoples of the network without disclosing their trust weights. Therefore, having a privacy-preserving system for computing trust of people and their content in the network would play a crucial role in collecting high-quality data from the people. In this paper, a novel trust model is proposed for evaluating the trust of the people in the social network without compromising the privacy of the participating people. The proposed systems have inherent properties of the trust weight assignment to a different class of user i.e. it can assign different weights to different users of the network, has decentralized setup, and ensures privacy properties under the malicious and honest but curious adversarial model. We evaluated the performance of the system by developing a prototype and applying it to different online social network dataset.
    Citation
    Azad, M., Perera, C., Bag, S., Barhamgi, M., and Hao, F. (2020). 'Privacy-preserving crowd-sensed trust aggregation in the user-centeric internet of people networks'. ACM Transactions on Cyber-Physical Systems, 5(1).
    Publisher
    ACM
    Journal
    ACM Transactions on Cyber-Physical Systems
    URI
    http://hdl.handle.net/10545/624948
    DOI
    10.1145/3446431
    Additional Links
    https://dl.acm.org/doi/abs/10.1145/3390860
    Type
    Article
    Language
    en
    ISSN
    2378-962X
    EISSN
    2378-9638
    ae974a485f413a2113503eed53cd6c53
    10.1145/3446431
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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