DOTNET PROJECT
Privacy-Preserving
Multi-Keyword Top-k k Similarity
Search Over Encrypted Data
Abstract:
Cloud computing
provides individuals and enterprises massive computing power and scalable
storage capacities to support a variety of big data applications in domains
like health care and scientific research, therefore more and more data owners
are involved to outsource their data on cloud servers for great convenience in
data management and mining. However, data sets like health records in
electronic documents usually contain sensitive information, which brings about
privacy concerns 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 data before outsourcing to the cloud servers, which
however reduces the data utility and makes many traditional data analytic
operators like keyword-based top- $k$ k document retrieval obsolete. In this
paper, we investigate the multi-keyword top- $k$ k search problem for big data
encryption against privacy breaches, and attempt to identify an efficient and
secure solution to this problem. Specifically, for the privacy concern of query
data, we construct a special tree-based index structure and design a random
traversal algorithm, which makes even the same query to produce different
visiting paths on the index, and can also maintain the accuracy of queries
unchanged under stronger privacy. For improving the query efficiency, we
propose a group multi-keyword top- $k$ k search scheme based on the idea of
partition, where a group of tree-based indexes are constructed for all
documents. Finally, we combine these methods together into an efficient and
secure approach to address our proposed top- $k$ k similarity search. Extensive
experimental results on real-life data sets demonstrate that our proposed
approach can significantly improve the capability of defending the privacy
breaches, the scalability and the time efficiency of query processing over the
state-of-the-art methods.
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