关键词: 云计算, 向量空间模型, 可搜索加密, 字典划分, 多关键词检索

Abstract: Aiming at the problems that the vector dimension generated by Traditional Vector Space Model (TVSM) is high, and the vector dot product operation to calculate the correlation between the documents and the queried keywords is time-consuming, a Dictionary Partition Vector Space Model (DPVSM) for encrypted ranked search in the cloud was proposed. First, the specific connotation definition of the DPVSM was given, and it was proved that the relevance score between the queried keywords and a document in DPVSM was exactly the same as that in TVSM. Then, by adopting the equal-length dictionary partition method, the encrypted vector generating algorithm and the relevance score calculation algorithm between documents and queried keywords were proposed. The experimental results show that the space occupation of document vectors in DPVSM is about 50% of that in TVSM, and the more the number of documents, the greater the gap. In addition, the space occupation of query vectors and the time consumption of relevance score calculation are also much lower than TVSM. Obviously, DPVSM is superior to TVSM in both the space cost of storing vectors and the time cost of relevance score calculation.

Key words: cloud computing, vector space model, searchable encryption, dictionary partition, multi-keyword search