A Review on Privacy Preserving Data Mining using Secure Multiparty Computation

Abstract

With the widespread habituate of data mining technology in the entire available sectors (public and private) elevate concerns about the sensitiveness of data being mined. Data mining is an enormously powerful technology to extract information from raw data. With the growth of ease of handiness of digital data the possibility of misapply of the data and the mined information grows. A key challenge is to build up security and privacy methods suitable for data mining. This is the reason PPDM (Privacy Preserving Data Mining) has acquired a steam in recent times. In this paper we have addressed the issue of PPDM and moreover, we have considered a scenario where two different parties possesses confidential databases of their own and wish to run a data mining algorithm on the union of their databases, without disclosing any unnecessary information, where we have suggested different methodologies in order to preserve the privacy in the data mining process one among them is Secure Multiparty Computation which is a field of cryptography. PPDM looks at the job of applying data mining algorithms on secret (confidential) data i.e. not granted to be disclosed even to the trusted party who’s running the algorithm.