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The advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. It defines and solve the challenging problem of privacy- preserving multi-keyword ranked ontology keyword mapping and search over encrypted cloud data (EARM), and establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. This mechanism that reduces the cost of encrypted matching, in the form of a pre-filtering operator using Bloom filters and simple randomization techniques. propose containment obfuscation techniques and provide a rigorous security analysis of the information leaked by Bloom filters in this case. Among various multi-keyword semantics, choose the efficient principle of “Enhanced Association Rule Mining coordinate matching”, i.e., as many matches as possible, to capture the similarity between search query and data documents, and further use “inner product similarity” to quantitatively formalize such principle for similarity measurement. First propose a basic EARM scheme using secure inner product computation, and then significantly improve it to meet different privacy requirements in two levels of threat models.