A Novel Privacy Preserving Keyword Search Scheme over Encrypted Cloud Data

Abstract

As the cloud computing becomes prevalent, data owners are motivated to outsource a large number of documents to the cloud for the great flexibility and convenience. Although encryption before outsourcing can keep user's data confidential, it raises a new challenge for users to retrieve some of the encrypted files containing specific keywords from the cloud. In this paper, we propose a novel privacy preserving keyword search scheme over encrypted cloud data to address this problem. To enable users to search over encrypted data, we firstly adopt a structure named as Inverted Matrix (IM) to build search index. The IM is consisted of a number of index vectors, each of which is associated with a keyword. Then we map a keyword to a value as an address used to locate the corresponding index vector. Finally, we mask index vectors with pseudo-random bits to obtain an Encrypted Enlarged Inverted Matrix (EEIM) to preserve the privacy of users. Through the security analysis, we show that our proposed scheme is secure.