Elsevier

Future Generation Computer Systems

Volume 124, November 2021, Pages 155-167
Future Generation Computer Systems

Blockchain-based decentralized reputation system in E-commerce environment

https://doi.org/10.1016/j.future.2021.05.035Get rights and content

Highlights

  • Our proposed system is the first work to design a DRS using the blockchain, IPFS and smart contract technologies for online shopping.

  • The reputation evaluation scheme is proposed to resist common attacks.

  • The monetary incentive mechanism is designed to form a virtuous circle.

Abstract

With the rapid development of E-commerce, more and more people are used to shopping online, in which the reputation scores of sellers play an important role in helping consumers purchase satisfactory products. However, in the existing E-commerce environments, the reputation scores of users (including sellers and buyers) are centrally computed and stored on a centralized cloud server, which might make errors or even engage in fraud and forgery. To address this issue, we propose a blockchain-based decentralized reputation system (BC-DRS) in the E-commerce environment, i.e., online shopping. In this system, the product information including product descriptions and comments is stored in the interplanetary file system (IPFS) and the corresponding address is returned, and the returned address and the reputation scores of users are stored on the blockchain. The reputation evaluation is implemented by designing and deploying a smart contract on the blockchain. Different from the traditional centralized reputation systems (CRSs), the proposed BC-DRS can protect the product information and users reputation scores from intentional and unintentional modifications, since it is very hard to change any data stored in the blockchain and IPFS. Also, as the reputation scores of users are computed and updated by all the ratings of their transactions weighted by the practical transaction factors, it is effective to resist the common attacks, i.e., unfair rating and collusion. In addition, this system also contains a monetary incentive mechanism for the evaluation, which is helpful to form a virtuous circle in online shopping. The proposed BC-DRS is simulated on the popular blockchain platform, i.e., Ethereum with Solidity language. The experimental results and analysis demonstrate that the proposed BC-DRS has desirable usability and reliability.

Introduction

Nowadays, with the rapid development of big data and network communication technologies [1], [2], a variety of E-commerce business models and companies have already emerged and applied in our daily life [3]. Due to the convenience and efficiency of E-commerce, more and more people prefer to shop online. In 2018, about 1.6 billion people worldwide shop online from online retailing platforms such as eBay, Amazon, Taobao, and Jingdong, and the total amount of online transaction has reached up to 2.3 trillion US dollars. Compared to physical store shopping, it is more convenient for consumers to choose online shopping. Consequently, online shopping has gradually become an indispensable part of people’s lives.

In online shopping, one of key issues for consumers is how to choose high-quality products with reasonable prices. In practice, if consumers intend to purchase a certain kind of products such as “sneakers”, they usually first view many sale pages of “sneakers”. In each of these pages, there is not only the sneakers descriptions including name, price, pictures, advertising content offered by the supplier and the comments made by previous consumers, but also the sellers reputation scores, as shown by a toy example in Fig. 1. The consumers can view the reputation scores of sellers as references for online shipping, and thus the reputation scores play an important role in helping consumers choose satisfactory products. In the existing online retailing platforms such as eBay, Amazon, Taobao, and Jingdong, the centralized reputation systems (CRSs) have been built to centrally process and store the reputation scores on their own servers.

However, there are three main issues in these CRSs, which are listed as follows.

(1) Since all the reputation scores are centrally computed and stored in the servers of retailing platforms, it is relatively easy for the malicious employees or outside attackers to modify these data, and thus the reliability of the CRSs will be compromised.

(2) As reported in [4], [5], there are a lot of fake comments and ratings on the famous retailing platform, i.e., Amazon, caused by the common attacks such as unfair rating attack and collusion attack. Since these CRSs compute the reputations scores without taking these attacks into account, they cannot resist these attacks effectively. As a result, the consumers may be misguided by these fake comments and ratings during the online shopping.

(3) There is lack of monetary incentive mechanism. In these CRSs, the consumers do not have enough motivation to submit their comments and ratings to the online retailing platforms and thus most of comments and ratings are the default data, mainly due to the lack of monetary incentive mechanism.

In recent years, the blockchain has become a promising decentralized technique to address the issues of the existing CRSs. Subsequently, a few decentralized reputation systems (DRSs) have been proposed [6], [7], [8] based on blockchain in E-commerce environment. However, they focus on the decentralized reputation management for simple transactions rather than online shopping, and the reputation evaluation is implemented without sufficient consideration of transaction factors such as transaction time, transaction amount and the previous reputation scores of users to prevent the common attacks.

Therefore, it is very necessary to build a blockchain-based DRS (BC-DRS) to compute and store the reputation scores for the popular E-commerce activity, i.e., online shopping. In this system, the users store the product information including the product descriptions and product comments in the interplanetary file system (IPFS) [9] and obtain the returned address, and then store the returned address and the reputation scores on the blockchain; The reputation evaluation is implemented by designing and deploying a smart contract on the blockchain. The main contributions of the proposed BC-DRS are concluded as follows:

  • (1)

    To the best of our knowledge, the proposed system is the first work to design a DRS using the blockchain, IPFS and smart contract technologies for online shopping. Different from the traditional CRSs in which both the product information and users reputation scores are directly stored in the servers of online retailing platforms, the proposed system stores the product information in the IPFS, and then stores reputation scores and the addresses of product information on blockchain. Due to the decentralized and distributed characteristics of blockchain and IPFS technologies, it is very hard to change these data stored in IPFS and the address broadcasted on blockchain.

  • (2)

    The reputation evaluation scheme is proposed to resist common attacks. In the proposed BC-DRS, to prevent the common attacks in online shopping, the reputation scores of users are computed and updated by all the ratings of their transactions weighted by the practical transaction factors including transaction time, transaction amount, and previous reputation scores of uses. Consequently, the proposed BC-DRS is effective to resist the common attacks, i.e., unfair rating attack and collusion attack.

  • (3)

    The monetary incentive mechanism is designed to form a virtuous circle. The designed monetary incentive mechanism makes the consumers to have more motivation to submit their comments and ratings, which is very helpful for the current consumers to choose satisfactory products. As a result, a virtuous circle can be formed in online shopping. The reminder of this paper is organized as follows. Section 2 introduces the related work. Section 3 elaborates the proposed BC-DRS for online shopping. The experimental results and analysis are given in Section 4, and conclusion is drawn in Section 5, respectively.

Section snippets

Blockchain.

The basic concept of blockchain was first proposed by Nakamoto Satoshi [10]. Generally, blockchain is defined as a distributed data structure without a central repository and authority, and it can be replicated and shared among the nodes of a network. A blockchain is composed of continuous blocks, each of which records and stores all the transaction data over a period of time. Also, except for the first block (called as genesis block), each block contains the cryptographic hash value of

The proposed BC-DRS for online shopping

In this section, the proposed BC-DRS for online shopping is introduced in detail. In Section 3.1, we introduce the architecture and roles of the proposed BC-DRS. In Section 3.2, we describe the proposed reputation evaluation scheme and monetary incentive mechanism. In Section 3.3, we elaborate the designed smart contract.

Experimental results and analysis

In this section, we first introduce the experimental environment. Then, we test and analyze the usability performance of proposed BC-DRS in the aspect of fee costs and time costs. Finally, the reliability of the proposed BC-DRS is also test and analyzed.

Conclusion

In this paper, we have presented the BC-DRS for online shopping. It takes the advantages of Ethereum blockchain, IPFS and smart contracts for storing product information and evaluating reputations of users in online shopping. Due to the distributed and decentralized characteristics of Ethereum blockchain and IPFS, it is very hard for attackers to tamper the product information and reputation data stored on the blockchain and IPFS. Moreover, by designing the reputation evaluation scheme with the

CRediT authorship contribution statement

Zhili Zhou: Methodology, Writing - original draft. Meimin Wang: Experiment environment building, Data collection, Validation, Visualization. Ching-Nung Yang: Idea providing. Zhangjie Fu: Writing - review & editing. Xingming Sun: Writing - review & editing. Q.M. Jonathan Wu: Validation.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61972205, U1836208, U1836110, in part by the National Key R&D Program of China under Grant 2018 YFB1003205, in part by Ministry of Science and Technology under Grant MOST 108-2221-E-259-009-MY2 and 109-2221-E-259-010, Taiwan, in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund, and in part by the Collaborative Innovation Center of Atmospheric

Zhili Zhou received the B.S. degree in communication engineering from Hubei University in 2007, and the M.S. and Ph.D. degrees in computer application from the School of Information Science and Engineering, Hunan University, in 2010 and 2014, respectively.

He is a Professor with the School of Computer and Software, Nanjing University of Information Science and Technology, China. Also, he was a Postdoctoral Fellow with the Department of Electrical and Computer Engineering, University of Windsor,

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    Zhili Zhou received the B.S. degree in communication engineering from Hubei University in 2007, and the M.S. and Ph.D. degrees in computer application from the School of Information Science and Engineering, Hunan University, in 2010 and 2014, respectively.

    He is a Professor with the School of Computer and Software, Nanjing University of Information Science and Technology, China. Also, he was a Postdoctoral Fellow with the Department of Electrical and Computer Engineering, University of Windsor, Canada. His current research interests include steganography, information hiding, image search, image/video copy detection, digital forensics, and deep learning.

    Meimin Wang received the B.S. degree from Nanjing University of Information Science & Technology in 2019, China. He is currently pursuing his M. D. degree in Nanjing University of Information Science & Technology, China. His research interest includes blockchain and information security.

    Ching-Nung Yang obtained his Ph. D. degree in Electrical Engineering from National Cheng Kung University. His B.S. and M.S. degrees, both were awarded in Department of Telecommunication Engineering from National Chiao Tung University. Dr. Yang served in National Dong Hwa University since 1999.

    His current title is Professor in Department of Computer Science and Information Engineering. He had been Visiting Professor to University of Missouri Kansas City, University of Milan, and University of Tokyo. He is currently a Fellow of IET (IEE) and an IEEE senior member. Professor Yang has done extensive researches on visual cryptography and secret image sharing, and is the chief scientist in both areas. In fact, a very important innovation of visual cryptography, the probabilistic visual cryptography, was firstly proposed by Professor Yang. His areas of interest include error correcting code, multimedia security, cryptography, and information security.

    Zhangjie Fu (Member, IEEE) received the Ph.D. degree in computer science from the School of Computer and Software, Hunan University, China, in 2012. He was a Visiting Scholar of computer science and engineering with The State University of New York at Buffalo from March 2015 to March 2016.

    He is currently a Professor of computer science and the Director of the Bigdata Security Lab, Nanjing University of Information Science and Technology, China. His research has been supported by the NSFC, PAPD, and GYHY. He is a member of the ACM. His research interests include the IoT security, outsourcing security, digital forensics, and network and information security.

    Xingming Sun (SM’07) received the B.S. degree in mathematics from Hunan Normal University, Hunan, China, in 1984, the M.S. degree in computing science from Dalian University of Science and Technology, Dalian, China, in 1988, and the Ph.D. degree in computer science from Fudan University, Shanghai, China in 2001. He is currently a Professor with the School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China. In 2006, he visited the University College London, U.K., and he was a Visiting Professor with the University of Warwick, Coventry, U.K., between 2008 and 2010. His research interests include network and information security, database security, and natural language processing. (Based on document published on 19 June 2020).

    Q. M. Jonathan Wu (M’92 – SM’09) received the Ph.D. degree in electrical engineering from the University of Wales, Swansea, U.K., in 1990.

    From 1995 to 2005, he was affiliated with the National Research Council of Canada, where he became a Senior Research Officer and a Group Leader. He is currently a Professor with the Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada. He has published over 300 peer-reviewed papers in computer vision, image processing, intelligent systems, robotics, and integrated microsystems. His current research interests include 3-D computer vision, active video object tracking and extraction, interactive multimedia, sensor analysis and fusion, and visual sensor networks.

    Dr. Wu is the Fellow of the Canadian Academy of Engineering and holds the Tier 1 Canada Research Chair in Automotive Sensors and Information Systems. He is, or was, Associate Editors of the IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Circuits and Systems for Video Technology, Cognitive Computation, and International Journal of Robotics and Automation. He has served on technical program committees and international advisory committees for many prestigious conferences.

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