Application of Secure Multiparty Computation in Privacy Preserving Data Mining

Abstract: Privacy preserving data mining is an area of research concerned with the issues of privacy thus providing a solution to minimize privacy threats in data mining. PPDM also helps in maximizing analysis outcome and also helps in minimizing the disclosure of individual or organizational private data. The existing system comprises of several privacy preserving techniques but all these techniques lacked in the parameters of Input privacy and correctness. With the use of Secure Multiparty Computation (SMC) more focus is given on the parameters of Input privacy and correctness with the goal of creating methods for parties to jointly compute a function over their inputs while keeping those inputs private. SMC helps in performing global computations on the private data with the help of several trusted third parties (TTP) so that there is no loss on data and privacy is maintained. The main aim is to implement this technique of SMC in the online transaction processes so as to make the transactions happening across the world as safe and secure as possible. The overall online transaction system developed must be user friendly and the privacy or confidentiality of the users have to be preserved so that in the near future the users taking part in the process do not hesitate in providing their details and the confidentiality for each user detail is maintained.

Keywords: Secure Multiparty Computation(SMC), trusted third party (TTP), PPDM.