MKAC: Efficient and Privacy-Preserving Multi- Keyword Ranked Query With Ciphertext Access Control in Cloud Environments

Haiyong Bao, Lu Xing, Honglin Wu, Menghong Guan, Na Ruan, Cheng Huang, Hong-Ning Dai

Abstract: With the explosion of Big Data in cloud environments, data owners tend to delegate the storage and computation to cloud servers. Since cloud servers are generally untrustworthy, data owners often encrypt data before outsourcing it to the cloud. Numerous privacy-preserving schemes for the multi-keyword ranked query have been proposed, but most of these schemes do not support ciphertext access control, which can easily lead to malicious access by unauthorized users, causing serious damage to personal privacy and commercial secrets. To address the above challenges, we propose an efficient and privacy-preserving multi-keyword ranked query scheme (MKAC) that supports ciphertext access control. Specifically, in order to enhance the efficiency of the multi-keyword ranked query, we employ a vantage point (VP) tree to organize the keyword index. Additionally, we develop a VP tree-based multi-keyword ranked query algorithm, which utilizes the pruning strategy to minimize the number of nodes to search. Next, we propose a privacy-preserving multi-keyword ranked query scheme that combines asymmetric scalar-product-preserving encryption with the VP tree. Furthermore, attribute-based encryption mechanism is used to generate the decryption key based on the query user’s attributes, which is then employed to decrypt the query results and trace any malicious query user who may leak the secret key. Finally, a rigorous analysis of the security of MKAC is conducted. The extensive experimental evaluation shows that the proposed scheme is efficient and practical.

External IDs:dblp:journals/tcc/BaoXWGRHD25