Privacy-Protecting Attribute-Based Conjunctive Keyword Search Scheme in Cloud Storage

Yang Chen,
Yang Liu,
Jin Pan,
Fei Gao,
Emmanouil Panaousis,


Cloud storage has been deployed in various real-world applications. But how to enable Internet users to search over encrypted data and to enable data owners to perform fine-grained search authorization are of huge challenge. Attribute-based keyword search (ABKS) is a well-studied solution to the challenge, but there are some drawbacks that prevent its practical adoption in cloud storage context. First, the access policy in the index and the attribute set in the trapdoor are both in plaintext, they are likely to reveal the privacy of data owners and users. Second, the current ABKS schemes cannot provide multi-keyword search under the premise of ensuring security and efficiency.
We explore an efficient way to connect the inner product encryption with the access control mechanism and search process in ABKS, and propose a privacy-protecting attribute-based conjunctive keyword search scheme. The proposed scheme provides conjunctive keyword search and ensures that the access policy and attribute set are both fully hidden. Formal security models are defined and the scheme is proved IND-CKA, IND-OKGA, access policy hiding and attribute set hiding. Finally, empirical simulations are carried out on real-world dataset, and the results demonstrate that our design outperforms other existing schemes in security and efficiency.


ABKS, Hidden access policy, Hidden attribute set, Conjunctive keyword search, Cloud storage

Citation Format:
Yang Chen, Yang Liu, Jin Pan, Fei Gao, Emmanouil Panaousis, "Privacy-Protecting Attribute-Based Conjunctive Keyword Search Scheme in Cloud Storage," Journal of Internet Technology, vol. 24, no. 1 , pp. 65-75, Jan. 2023.

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