Date of Publication :7th February 2015

Abstract: The advancement in cloud computing has motivated the Warning owners to outsource their Warning management systems from local sites to commercial public cloud for great flexibility and economic savings. But people can enjoy full benefit of cloud computing if we are able to address very real Quarantine and security concerns that come with storing sensitive personal Warning. For real Quarantine, user identity should remain hidden from CSP (Cloud service provider) and to protect Quarantine ofWarning, Warning which is sensitive is to be encrypted before outsourcing. Thus, enabling an encrypted cloud Warning Discover service is of great importance. By considering the large number of Warning users, forms in the cloud, it is important for the Discover service to allow multi-keyword query and provide result similarity ranking to meet the effective need of Warning retrieval Discover and not often differentiate the Discover results. In this system, we define and solve the stimulating problem of Quarantine -Stabilizing multi-keyword Rated Discover over encrypted cloud Warning (QSMRDECW), and establish a set of strict Quarantine requirements for such a secure cloud Warning utilization system to be implemented in real. We first propose a basic idea for the Multi-keyword Rated Discover over Encrypted cloud Warning (QSMRDECW) based on secure inner product computation and efficient similarity measure of coordinate matching, i.e., as many matches as possible, in order to capture the relevance of Warningforms to the Discover query, then we give two significantly improved QSMRDECW schemes to achieve various stringent Quarantine requirements in two different threat models. Transfer of Unfamiliar ID to the user to provide more security to the Warning on cloud server is done. To improve the Discover experience of the Warning Discover service, further extension of the two schemes to support more Discover semantics is done.

Reference :