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PrivBox: Verifiable decentralized reputation system for online marketplaces

Lookup NU author(s): Dr Muhammad Azad, Dr Samiran Bag, Professor Feng Hao

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

© 2018 Elsevier B.V. In online marketplaces (e-commerce, cloud marketplaces), potential buyers/consumers do not have direct access to inspect the quality of products and services offered by service providers or retailers of the marketplace. Therefore, consumers have to trust the reputation system of the online marketplace for deciding whether or not to interact with the particular service provider. Consumer's feedback about the service provider plays an important role to evaluate the trustworthiness of the service provider, but it brings the challenge of security and privacy of the feedback providers. Existing centralized reputation systems collect feedback from consumers about their service providers but they leak sensitive information about consumers transactions (such as buying history, likes and dislikes). To ensure the privacy of consumers, this paper presents a privacy-preserving decentralized reputation system named PrivBox that protects consumer's feedback values using homomorphic cryptographic methods and zero-knowledge proof primitives in a decentralized way. The design of PrivBox ensures the following characteristics. 1) It ensures the privacy of consumers without the use of any trusted setup or trusted third party, 2) it ensures that consumer's provided feedback value remains within the prescribed range, and 3) it enables consumers and service providers to verify the aggregated reputation without relying on any trusted third party. PrivBox achieves privacy-preservation properties using an encrypted exchange of feedback values and ensures well-formedness of encrypted values using zero-knowledge proof of knowledge. To evaluate the performance, we implement a prototype of the proposed system. The results demonstrate that our solution preserves privacy of participants while incurring only small computation and bandwidth overheads.


Publication metadata

Author(s): Azad MA, Bag S, Hao F

Publication type: Article

Publication status: Published

Journal: Future Generation Computer Systems

Year: 2018

Volume: 89

Pages: 44-57

Print publication date: 01/12/2018

Online publication date: 21/06/2018

Acceptance date: 27/05/2018

Date deposited: 09/07/2018

ISSN (print): 0167-739X

ISSN (electronic): 1872-7115

Publisher: Elsevier BV

URL: https://doi.org/10.1016/j.future.2018.05.069

DOI: 10.1016/j.future.2018.05.069


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