An Intelligent Approach for Protecting Privacy in Distributed Information Mining Using Secured Computation of Multiple Participating Sites
Vibhor Sharma, Shashi Bhushan, Bhim Singh Boahar, Pramod Kumar, Anuj Kumar
Abstract
Information mining is a very well-known task using which managers can take a better decision regarding data operations. For that purpose, they need to get useful information from a large amount of raw data. This kind of big data mining is usually carried out on unstructured data that is huge in context of its size. Due to the vast size, the data mining process faces confidentiality and security breaches issues. There are many technologies through which we can search for useful information for getting fruitful results towards the fulfilment of organizational goals. However, it's a big challenge for researchers to get knowledge from the large amount of data that is owned by multiple parties which are located at different sites. For that, they need to perform Distributed Data Mining task with the concern of privacy leakage. While secure multiparty computation presents the solution to this problem but there are some issues which are untouched yet. In this paper, we presented two data privacy issues not even solved by multiparty computation. The presented algorithms are designed using the process of matrix computation with the help of encoding and decomposition methods. The implementation of the work is based on a secure Multi-Party Computation (MPC) protocol named SPDZ so that we can perform the sharing of data values. We analyzed the results produced by single machine and proposed design and implementation to show the similarity between both. Experimental results show the effectiveness of implemented algorithms and their implementation to preserve the privacy of distributed data in the process of distributed data mining.