PROJECT TITLE :
Privacy-Preserving Multi-keyword Top-k Similarity Search Over Encrypted Data - 2017
Cloud computing provides people and enterprises large computing power and scalable storage capacities to support a variety of big data applications in domains like health care and scientific analysis, therefore more and additional knowledge homeowners are involved to outsource their information on cloud servers for great convenience in information management and mining. But, data sets like health records in electronic documents sometimes contain sensitive data, which brings concerning privacy issues if the documents are released or shared to partially untrusted third-parties in cloud. A practical and widely used technique for data privacy preservation is to encrypt information before outsourcing to the cloud servers, which but reduces the information utility and makes several traditional data analytic operators like keyword-based mostly prime-k document retrieval obsolete. In this paper, we investigate the multi-keyword prime-k search drawback for giant knowledge encryption against privacy breaches, and attempt to spot an economical and secure resolution to this problem. Specifically, for the privacy concern of query knowledge, we have a tendency to construct a special tree-based index structure and style a random traversal algorithm, which makes even the same query to produce totally different visiting ways on the index, and will additionally maintain the accuracy of queries unchanged beneath stronger privacy. For improving the question efficiency, we have a tendency to propose a cluster multi-keyword top-k search scheme based on the thought of partition, where a group of tree-primarily based indexes are made for all documents. Finally, we mix these methods together into an efficient and secure approach to address our proposed top-k similarity search. Extensive experimental results on real-life data sets demonstrate that our proposed approach can considerably improve the potential of defending the privacy breaches, the scalability and the time efficiency of question processing over the state-of-the-art methods.
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