Enabling efficient and accurate semantic search over encrypted cloud data

Abstract

The privacy and security of cloud data have drawn much attention, leading to more data owners outsourcing encrypted data. However, the common practice of encryption can reduce data searchability. Semantic searchable encryption aims to support flexible queries over encrypted data and achieve efficient search while ensuring that search results match the user's search intent. Although semantic searchable encryption schemes have made progress, they still have limitations in properly balancing accuracy, efficiency, and security. In this paper, we propose a novel Context-Enhanced Semantic Searchable Encryption (CESSE) scheme to achieve accurate and highly efficient secure semantic search over encrypted cloud data. To achieve it, we first adopt a context-enhanced pre-trained model component to mine the relevance between queries and documents by contrastive learning and obtain context-enhanced vector representations to improve search accuracy. Then, to ensure privacy protection, we utilize an optimized asymmetric scalar-product-preserving encryption (optimized ASPE) algorithm to encrypt vectors before outsourcing to the cloud. Additionally, we construct the approximate nearest neighbor (ANN) index to accelerate vector searching. At last, we give a formal definition of security and theoretically prove the safety of our scheme under a more practical threat model. Extensive experiments demonstrate that the CESSE outperforms state-of-the-art baselines with better accuracy and efficiency.