A Verifiable Dynamic Multiuser Searchable Encryption Scheme without Trusted Third Parties Searchable encryption is a cryptographic primitive that allows users to search for keywords on encrypted data. It allows users to search in archives stored on cloud servers. Among searchable encryption schemes, those supporting multiuser settings are more suitable for daily application scenarios and more practical. However, since the cloud server is semitrusted, the result set returned by the server is undefined, and most existing multiuser searchable encryption schemes rely heavily on trusted third parties to manage user permission. To address these problems, verifiable multiuser searchable encryption schemes with dynamic management of user search permissions, weak trust on trusted third parties and are desirable. In this paper, we propose such a scheme. Our scheme manages user permission and key distribution without a trusted third party. User search permission and user access permission matrices are generated separately to manage user permissions dynamically. In addition, our scheme can verify the result set returned by the cloud server. We also show that our scheme is index and trapdoor indistinguishable under chosen keyword attacks in the random oracle model. Finally, a detailed comparison experiment is made by using the actual document data set, and the results show that our scheme is efficient and practical.

A Multibranch Search TreeBased MultiKeyword Ranked Search Scheme over Encrypted Cloud Data In the interest of privacy concerns, cloud service users choose to encrypt their personal data before outsourcing them to cloud. However, it is difficult to achieve efficient search over encrypted cloud data. Therefore, how to design an efficient and accurate search scheme over largescale encrypted cloud data is a challenge. In this paper, we integrate bisecting kmeans algorithm and multibranch tree structure and propose the xcexb1filtering tree search scheme based on bisecting kmeans clusters. The novel index tree is built from bottomup, and a greedy depth first algorithm is used for filtering the nonrelevant document cluster by calculating the relevance score between the filtering vector and the query vector. The xcexb1filtering tree can improve the efficiency without the loss of search accuracy. The experiment on a realworld dataset demonstrates the effectiveness of our scheme.