Privacy Preserved Collaborative Secure Multiparty Data Mining

Abstract

Problem statement: In the current modern business environment, its success is defined by collaboration, team efforts and partnership, rather than lonely spectacular individual efforts in isolation. So the collaboration becomes especially important because of the mutual benefit it brings. Sometimes, such collaboration even occurs among competitors, or among companies that have conflict of interests, but the collaborators are aware that the benefit brought by such collaboration will give them an advantage over other competitors. Approach: For this kind of collaboration, data's privacy becomes extremely important: all the parties of the collaboration promise to provide their private data to the collaboration, but neither of them wants each other or any third party to learn much about their private data. One of the major problems that accompany with the huge collection or repository of data is confidentiality. The need for privacy is sometimes due to law or can be motivated by business interests. Results: Performance of privacy preserving collaborative data using secure multiparty computation is evaluated with attack resistance rate measured in terms of time, number of session and participants and memory for privacy preservation. Conclusion: Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed, even to the party running the algorithm.