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A searchable encryption scheme that specifically supports genetic data was proposed to protect users from privacy risks. The existing searchable encryption schemes only support keywords search， they cannot be applied to genetic data without keywords. So， a security index using cryptographic primitives such as suffix trees and pseudo-random functions was constructed to implement arbitrary substring searches for ciphertext genomic data. The safety proof indicated that the method satisfies the dynamic adaptive security， and the efficiency is evaluated by both theoretical analysis and real data. This scheme can perform efficient and safe arbitrary substring search on genomic data，protect data integrity and privacy and has broad application prospects in the environment of personalized medical popularization. [ABSTRACT FROM AUTHOR]

This paper proposes a searchable encryption model for graph data to address the problem that most of the current searchable encryption schemes cannot deal with complex data structures. The algorithms and security models of the scheme are described. Taking the encryption index of matrix structure into account， the detailed description of the searchable encryption scheme for graph data that supports adjacency queries is presented. The scheme utilizes pseudo-random functions and pseudo-random permutations to protect the privacy of the graph data and the encrypted index. Real models and ideal models test the security of the scheme. Compared with traditional searchable encryption schemes， the proposed scheme has more flexible searching ability and higher efficiency， which promotes broader application under big data environment. [ABSTRACT FROM AUTHOR]