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A trustworthy online recommendation system based on social connections in a privacy-preserving manner

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Abstract

Certain consumer websites provide reviews from previous buyers to help new customers make purchasing decisions. However, fake reviews can have an adverse impact on user trust. Most previous suggestions for addressing this problem are still subject to various security concerns in terms of privacy, reliability, and authenticity. To ensure the security of online review systems, this paper proposes the development of a secure online-evaluation method based on social connections to establish evaluation authenticity and provide protection against evaluation forgery while preserving the reviewer’s identity. The proposed method enables users to recognize evaluations from their friends to identify reviews from more trustworthy sources, and authenticates online reviews to prevent possible forgery. In addition, it preserves the privacy of friendship relationships from application server and other users and identifier relations between the personal identifier and online identifier. The proposed approach can be applied to Internet auctions and online games, and is shown to be secure and efficient, with sufficient matching probability to be practical.

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Acknowledgments

This work is partially supported by the MOST project under Grant MOST 104-2221-E-182-012. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

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  1. Department of Electrical Engineering, Chang Gung University, 259 Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan, Taiwan

    Shin-Yan Chiou

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  1. Shin-Yan Chiou
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Correspondence to Shin-Yan Chiou.

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Chiou, SY. A trustworthy online recommendation system based on social connections in a privacy-preserving manner. Multimed Tools Appl 76, 9319–9336 (2017). https://doi.org/10.1007/s11042-016-3534-8

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