Abstract

The advent of cloud computing, data owners are motivated to outsource their complex\ndata management systems from local sites to commercial public cloud for great flexibility and\neconomic savings. But for protecting data privacy, sensitive data has to be encrypted before\noutsourcing, which obsoletes traditional data utilization based on plaintext keyword search.\nThus, enabling an encrypted cloud data search service is of paramount importance. Considering\nthe large number of data users and documents in cloud, it is crucial for the search service to allow\nmulti-keyword query and provide result similarity ranking to meet the effective data retrieval\nneed. Related works on searchable encryption focus on single keyword search or Boolean\nkeyword search, and rarely differentiate the search results. In this paper, for the first time, we\ndefine and solve the challenging problem of privacy-preserving multi-keyword ranked ontology\nkeyword mapping and search over encrypted cloud data (DSMK), and establish a set of strict\nprivacy requirements for such a secure cloud data utilization system to become a reality. Among\nvarious multi-keyword semantics, we choose the efficient principle of “Dual Server Multi\nkeyword data search”, i.e., as many matches as possible, to capture the similarity between search\nquery and data documents, and further use “inner product similarity” to quantitatively formalize\nsuch principle for similarity measurement. We first propose a basic DSMK scheme using secure\ninner product computation, and then significantly improve it to meet different privacy\nrequirements in two levels of threat models. Thorough analysis investigating privacy and\nefficiency guarantees of proposed schemes is given, and experiments on the real-world dataset\nfurther show proposed schemes indeed introduce low overhead on computation and\ncommunication.