Verifiable Fuzzy Multi-keyword Search over Encrypted Data with Adaptive Security

Abstract

To ensure the security of outsourced data without affecting data availability, one can use Symmetric Searchable Encryption (SSE) to achieve search over encrypted data. Considering that query users may search with misspelled words, the fuzzy search should be supported. However, conventional privacy-preserving fuzzy multi-keyword search schemes are incapable of achieving the result verification and adaptive security. To solve the above challenging issues, in this paper we propose a Verifiable Fuzzy multi-keyword Search scheme with Adaptive security (VFSA). VFSA first employs the locality sensitive hashing to hash the misspelled and correct keywords to the same positions, then designs a twin Bloom filter for each document to store and mask all keywords contained in the document, next constructs an index tree based on the graph-based keyword partition algorithm to achieve adaptive sublinear retrieval, finally combines the Merkle hash tree structure with the adapted multiset accumulator to check the correctness and completeness of search results. Our formal security analysis shows that VFSA is secure under the IND-CKA2 model and achieves query authentication. Our empirical experiments using the real-world dataset demonstrate the practicality of VFSA.