Identity-Based Private Matching over Outsourced Encrypted Datasets

Abstract

With wide use of cloud computing and storage services, sensitive information is increasingly centralized into the cloud to reduce the management costs, which raises concerns about data privacy. Encryption is a promising way to maintain the confidentiality of outsourced sensitive data, but it makes effective data utilization to be a very challenging task. In this paper, we focus on the problem of private matching over outsourced encrypted datasets in identity-based cryptosystem that can simplify the certificate management. To solve this problem, we propose an Identity-Based Private Matching scheme ( $\mathsf{IBPM}$ ), which realizes fine-grained authorization that enables the privileged cloud server to perform private matching operations without leaking any private data. We present the rigorous security proof under the Decisional Linear Assumption and Decisional Bilinear Diffie-Hellman Assumption. Furthermore, through the analysis of the asymptotic complexity and the experimental evaluation, we verify that the cost of our $\mathsf{IBPM}$ scheme is linear to the size of the dataset and it is more efficient than the existing work of Zheng and Xu [30] . Finally, we apply our $\mathsf{IBPM}$ scheme to build two efficient schemes, including identity-based fuzzy private matching as well as identity-based multi-keyword fuzzy search.