Open Access

ARTICLE

An Effective Security Comparison Protocol in Cloud Computing

Yuling Chen^1,2, Junhong Tao¹, Tao Li^1,*, Jiangyuan Cai³, Xiaojun Ren⁴

1 State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550000, China
2 Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology, Guilin, 550000, China
3 POWERCHINA Guizhou Electric Power Engineering Co., Ltd., Guiyang, 550000, China
4 Blockchain Laboratory of Agricultural Vegetables, Weifang University of Science and Technology, Weifang, 261000, China

* Corresponding Author: Tao Li. Email:

Computers, Materials & Continua 2023, 75(3), 5141-5158. https://doi.org/10.32604/cmc.2023.037783

Received 06 November 2022; Accepted 22 February 2023; Issue published 29 April 2023

Abstract

With the development of cloud computing technology, more and more data owners upload their local data to the public cloud server for storage and calculation. While this can save customers’ operating costs, it also poses privacy and security challenges. Such challenges can be solved using secure multi-party computation (SMPC), but this still exposes more security issues. In cloud computing using SMPC, clients need to process their data and submit the processed data to the cloud server, which then performs the calculation and returns the results to each client. Each client and server must be honest. If there is cooperation or dishonest behavior between clients, some clients may profit from it or even disclose the private data of other clients. This paper proposes the SMPC based on a Partially-Homomorphic Encryption (PHE) scheme in which an addition homomorphic encryption algorithm with a lower computational cost is used to ensure data comparability and Zero-Knowledge Proof (ZKP) is used to limit the client's malicious behavior. In addition, the introduction of Oblivious Transfer (OT) technology also ensures that the semi-honest cloud server knows nothing about private data, so that the cloud server of this scheme can calculate the correct data in the case of malicious participant models and safely return the calculation results to each client. Finally, the security analysis shows that the scheme not only ensures the privacy of participants, but also ensures the fairness of the comparison protocol data.

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Keywords

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