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Privacy-assured Boolean Adjacent Vertex Search over Encrypted Graph Data in Cloud Computing
( Hong Zhu ),( Bin Wu ),( Meiyi Xie ),( Zongmin Cui ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.10
With the popularity of cloud computing, many data owners outsource their graph data to the cloud for cost savings. The cloud server is not fully trusted and always wants to learn the owners` contents. To protect the information hiding, the graph data have to be encrypted before outsourcing to the cloud. The adjacent vertex search is a very common operation, many other operations can be built based on the adjacent vertex search. A boolean adjacent vertex search is an important basic operation, a query user can get the boolean search results. Due to the graph data being encrypted on the cloud server, a boolean adjacent vertex search is a quite difficult task. In this paper, we propose a solution to perform the boolean adjacent vertex search over encrypted graph data in cloud computing (BASG), which maintains the query tokens and search results privacy. We use the Gram-Schmidt algorithm and achieve the boolean expression search in our paper. We formally analyze the security of our scheme, and the query user can handily get the boolean search results by this scheme. The experiment results with a real graph data set demonstrate the efficiency of our scheme.
EFTG: Efficient and Flexible Top-K Geo-textual Publish/Subscribe
( Hong Zhu ),( Hongbo Li ),( Zongmin Cui ),( Zhongsheng Cao ),( Meiyi Xie ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.12
With the popularity of mobile networks and smartphones, geo-textual publish/subscribe messaging has attracted wide attention. Different from the traditional publish/subscribe format, geo-textual data is published and subscribed in the form of dynamic data flow in the mobile network. The difference creates more requirements for efficiency and flexibility. However, most of the existing Top-k geo-textual publish/subscribe schemes have the following deficiencies: (1) All publications have to be scored for each subscription, which is not efficient enough. (2) A user should take time to set a threshold for each subscription, which is not flexible enough. Therefore, we propose an efficient and flexible Top-k geo-textual publish/subscribe scheme. First, our scheme groups publish and subscribe based on text classification. Thus, only a few parts of related publications should be scored for each subscription, which significantly enhances efficiency. Second, our scheme proposes an adaptive publish/subscribe matching algorithm. The algorithm does not require the user to set a threshold. It can adaptively return Top-k results to the user for each subscription, which significantly enhances flexibility. Finally, theoretical analysis and experimental evaluation verify the efficiency and effectiveness of our scheme.