Privacy Preserving Multiparty Collaborative Data Mining for Multiple Service Providers

The emergence of Application Service Providers hosting Internet-based data mining services is being seen as available alternative for organisations that value their knowledge resources but are constrained by the high cost of data mining software in this paper, we present a new multiple service provider model of operation for the Internet delivery of data mining services. This model has several advantages over the currently predominant approach for delivering data mining, services such as providing clients with a wider variety of options, choice of service providers and the benefits of a more competitive marketplace. 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. 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. 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.