Performance analysis of privacy preserving distributed data mining based on cryptographic techniques

Abstract

Privacy Preservation should be maintained while retrieving relevant information from multiple sources without any leakage. In order to make correct decision, Data mining plays a crucial role in any business environment. Performing distributed data mining for retrieving or extracting relevant information from multiple parties without any leakage is an important issue which has to be addressed. In order to overcome this problem, privacy preserving distributed data mining methods was used for secure and reliable information sharing from multiple parties. In this paper, different privacy preservation distributed data mining techniques commonly known as cryptographic approaches like Secure Multiparty Computation Homomorphic Encryption and Secret Sharing methods were discussed and methods like homomorphic encryption and secret sharing is implemented on medical and business data. Our experimental result shows that secret sharing method performs better than homomorphic encryption.