While users enjoy the convenience of data outsourcing in the cloud, they also face the risks of data modification and private information leakage. Searchable encryption technology can perform keyword searches over encrypted data while protecting their privacy and guaranteeing the integrity of the data by verifying the search results. However, some associated problems are still encountered, such as the low efficiency of verification and uncontrollable query results. Accordingly, this paper proposes a Privacy-Preserving Searchable Encryption (PPSE) scheme based on public and private blockchains. First, we store an encrypted index in a private blockchain while outsourcing corresponding encrypted documents to a public blockchain. The encrypted documents are located through the encrypted index. This method can reduce the storage overhead on the blockchains, and improve the efficiency of transaction execution and the security of stored data. Moreover, we adopt a smart contract to introduce a secondary verification access control mechanism and restrict data users’ access to the private blockchain through authorization for the purpose of guaranteeing data privacy and the correctness of access control verification. Finally, the security analysis and experimental results indicate that compared with existing schemes, the proposed scheme can not only improve the security of encrypted data but also guarantee the efficiency of the query.

Attribute-Based Encryption (ABE) has been widely used for ciphertext retrieval in the cloud environment. However, bi-flexible attribute control and privacy keywords are difficult problems that have yet to be solved. In this paper, we introduce the denial of access policy and the mutual matching algorithm of a dataset used to realize bidirectional control of attributes in the cloud server. To solve the problem of keyword privacy, we construct a security trapdoor by adding random numbers that effectively resist keyword guessing attacks from cloud servers and external attackers. System security is reduced to the Deterministic Bilinear Diffie-Hellman (DBDH) hypothesis problem. We validate our scheme through theoretical security analysis and experimental verification. Experiments are conducted on a real dataset, and results show that the scheme has higher security and retrieval efficiency than previous methods.