Multi-keyword Ranked Query Over Encrypted Data In Cloud Computing

In cloud computing, security and privacy become major concerns when data owners outsource their private data onto public cloud servers which are not within their trusted management domains. To avoid information leakage, sensitive data has to be encrypted before uploading onto the cloud servers, which makes it a big challenge to support efficient keyword based queries and rank the matching results on the encrypted data. In the current multi-keyword ranked search approach, the keyword dictionary is static and cannot be extended easily when the number of keywords increases. Furthermore, it does not take the user behavior and keyword access frequency into account. For the query matching result, the out-of-order ranking problem may occur. This makes it is hard for the data consumer to find the subset which most likely satisfying its requirement.Encrypted vector scheme can be flexible employed to solve multi-keyword ranked query problem over encryption data in cloud computing. We introduce the theory used in vector query and analyze its advantages over other solutions firstly. Then, we propose an efficient multi-keyword ranked query over encrypted data scheme called MKQE. Based on MKQE, we design vector scheme to build index, which takes privacy-preserving and scalability into consideration. Partitioned matrices is used to secret key generation, which greatly reduces the maintenance overhead during the keyword dictionary expansion. Finally, we optimize and improve MKQE scheme based on encryption access control to reduce the file key management overload. It takes keyword weights and user access history into considerations when generating the query result. Linked list structure is employed to optimize the secret key, which greatly improves the performance.Our experiments show that MKQE greatly reduces the index encryption time, improves the accuracy and validity of results and reduces file key management load over the current solutions.