With the appearance of cloud computing, data proprietors are motivated to outsource their complex records management systems from local websites to the industrial public cloud for great flexibility and monetary financial savings. But for protecting information privacy, sensitive records should be encrypted before than outsourcing, which obsoletes traditional data utilization based on plaintext key-word search. Therefore, allowing an encrypted cloud data search provider is of paramount significance. Considering the large number of data users and documents within the cloud, it’s far necessary to allow multiple key phrases within the search request and return files inside the order in their relevance to these key words. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. On this project, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted facts in cloud computing (MRSE). We set up a set of strict privacy necessities for such secure cloud facts usage machine. Among diverse multi-key-word semantics, we choose the efficient similarity measure of “coordinate matching,” i.e., as many matches as possible, to caputre the relevance of records documents to the search query. We further use “inner product similarity” to quantitatively examine such similarity measure. We first advocate a simple concept for the MRSE primarily based on comfortable internal product computation, after which give two notably advanced MRSE schemes to attain numerous stringent privacy necessities in two distinct threat models. To improve search revel in of the data seek carrier, we further amplify these two schemes to help more seek semantics. Thorough analysis investigating privateness and performance guarantees of proposed schemes is given. Experiments at the real-world data set further display proposed schemes certainly introduce low overhead on computation and communication.