Research Of Privacy-Preserving Multi-Keyword Top-k Search Over Encrypted Hybrid Cloud Data

With the rapid development of cloud computing services,more and more individuals and enterprises prefer to outsource their data or computing to clouds.In order to preserve data privacy,the data should be encrypted before outsourcing and it is a challenge to perform searches over encrypted dataIn this paper,we first propose a privacy-preserving multi-keyword top-k search scheme over encrypted data in hybrid cloud,which is denoted as MTSE-HC.The keyword dictionary of documents is clustered into balanced partitions by a bisecting k-means clustering based keyword partition algorithm.According to the partitions,the keyword partition based bit vectors are adopted for documents and queries which are utilized as the index of searches.The private cloud filters out the candidate documents by the keyword partition based bit vectors,and then the public cloud uses the trapdoor to determine the result in the candidates.On the basis of the MTSE-HC scheme,an enhancement scheme EMTSE-HC is proposed,which adds complete binary pruning tree to further improve search efficiency.In addition,in order to be suitable for large-scale data processing application scenarios,we use the Hadoop distributed platform,HDFS distributed file storage system,and Map-Reduce computing framework to propose a parallel privacy-preserving multi-keyword top-k search scheme over encrypted data in hybrid cloud,which is denoted as PMTSE-HCThe security analysis and performance evaluation show that MTSE-HC and EMTSE-HC are privacy-preserving multi-keyword ranked search schemes for hybrid clouds and outperforms the existing scheme FMRS in terms of search efficiency.Meanwhile,under the background of large-scale data application,the search efficiency of PMTSE-HC is better than MTSE-HC.