| Title | DPP: Data Privacy-Preserving for Cloud Computing based on Homomorphic Encryption |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Wang, Jing, Wu, Fengheng, Zhang, Tingbo, Wu, Xiaohua |
| Conference Name | 2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) |
| Date Published | oct |
| Keywords | cloud computing, data privacy, homomorphic encryption, Human Behavior, human factors, Knowledge discovery, Metrics, Privacy-preserving, pubcrawl, Public key, reliability, resilience, Resiliency, Scalability, Servers |
| Abstract | Cloud computing has been widely used because of its low price, high reliability, and generality of services. However, considering that cloud computing transactions between users and service providers are usually asynchronous, data privacy involving users and service providers may lead to a crisis of trust, which in turn hinders the expansion of cloud computing applications. In this paper, we propose DPP, a data privacy-preserving cloud computing scheme based on homomorphic encryption, which achieves correctness, compatibility, and security. DPP implements data privacy-preserving by introducing homomorphic encryption. To verify the security of DPP, we instantiate DPP based on the Paillier homomorphic encryption scheme and evaluate the performance. The experiment results show that the time-consuming of the key steps in the DPP scheme is reasonable and acceptable. |
| DOI | 10.1109/CyberC55534.2022.00016 |
| Citation Key | wang_dpp_2022 |
DPP: Data Privacy-Preserving for Cloud Computing based on Homomorphic Encryption