Dynamic Multi-keyword Top-k Ranked Search over Encrypted Cloud Data

Nowadays, more and more people are motivated to outsource their local data to public cloud servers for great convenience and reduced costs in data management. But in consideration of privacy issues, sensitive data should be encrypted before outsourcing, which obsoletes traditional data utilization like keyword-based document retrieval. In this paper, we present a secure and efficient multi-keyword ranked search scheme over encrypted data, which additionally supports dynamic update operations like deletion and insertion of documents. Specifically, we construct an index tree based on vector space model to provide multi-keyword search, which meanwhile supports flexible update operations. Besides, cosine similarity measure is utilized to support accurate ranking for search result. To improve search efficiency, we further propose a search algorithm based on "Greedy Depth-first Traverse Strategy". Moreover, to protect the search privacy, we propose a secure scheme to meet various privacy requirements in the known ciphertext threat model. Experiments on the real-word dataset show the effectiveness and efficiency of proposed scheme.