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Published November 22, 2018 | Version v1
Journal article Open

Peer Prediction-Based Trustworthiness Evaluation and Trustworthy Service Rating in Social Networks

  • 1. Department of Electronic Engineering, Ts- inghua University, Beijing 100084, P. R. China
  • 2. Electric and Electronic Engineering Department, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
  • 3. Tsinghua Space Center, Tsinghua University, Beijing 100084, China, and also with the Key Laboratory of EDA, Research Insti- tute of Tsinghua University in Shenzhen, Shenzhen 518057, China
  • 4. Advanced Innovation Center for Materials Genome Engineering, Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China
  • 5. Department of Electrical Engineering, Princeton University, Princeton, NJ 08544 USA

Description

With the development of online applications based on the social network, many different approaches of service
to achieve these applications have emerged. Users’ reporting and sharing of their consumption experience or opinion can be utilized to rate the quality of different approaches of online services. How to ensure the authenticity of the users’ reports and identify malicious ones with cheating reports become important issues to achieve an accurate service rating. In this paper, we provide a private-prior peer prediction mechanism based trustworthy service rating system with a data processing center (DPC), which requires users to report to it with their prior and posterior believes that their peer users will report a high quality opinion of the service. The DPC evaluates users’ trustworthiness with their reports by applying the strictly proper scoring rule, and removes reports received from users with low
trustworthiness from the service rating procedure. This peer prediction method is incentive compatible and able to motivate users to report honestly. In addition, to identify malicious users and bad-functioning/unreliable users with high error rate of quality judgement, an unreliability index is proposed in this paper to evaluate the uncertainty of reports. Reports with high unreliability values will also be excluded from the service rating system. By combining the trustworthiness and unreliability, malicious users will face a dilemma that they cannot receive a
high trustworthiness and low unreliability at the same time when they report falsely. Simulation results indicate that the proposed peer prediction based trustworthy service rating can identify malicious and unreliable behaviours effectively, and motivate users to report truthfully. The relatively high service rating accuracy can be achieved by the proposed system.

Files

PeerPredictionBasedTrustworthinessEvaluationandTrustworthyServiceRatinginSocialNetworks-1.pdf

Additional details

Funding

SerIoT – Secure and Safe Internet of Things 780139
European Commission