Privacy Preserving Secure Communication Pattern using Cryptographic Technique via Trusted Third Party (TTP) Model in Distributed Data Mining

As security plays an important role in most of the applications where small change of data can lead to major problems. Thus, to secure the information there is need of a stronger encryption, which is very hard to break. In order to achieve better results and improve security, several levels of encryption have to be applied to the information and the way of encryption should not be vulnerable to attacks. Some of the conventional encryption methods are implemented for encryption, but can be cracked easily with the high end technologies. The goal of this paper is to develop multi-level encrypting technique that can be used to encrypt top-secret data in the database. In this information era, data mining has emerged as a way for distinguishing patterns from huge quantities of information. Some database may contain private or personal data which should not be leaked out. Thus techniques of data mining without leaking the private information are needed. Also there is a demand for a stronger encryption which is very hard to crack. In this paper, we proposed a multi level of multiple encryption schemes for secure communication pattern (SCP) which enhances the security of the private data in database during data mining. In the recent past years, Privacy Preserving Data Mining (PPDM) has attracted research interest with potential for wide applications. Many techniques such as randomization, cryptography and anonymity have been experimented with privacy preserving data mining. The work considers information system based approach as not all attributes may store same level of sensitive data. Therefore, some attribute values may require higher degree of privacy preservation than some others. Here we explore the use of cryptography, namely Multilevel Encryption and Decryption (MLED) algorithm for encrypted data sharing to achieve privacy preservation. We focus on privately Secure Communication Patterns (SCP) that refers to a contributively relation of participants in computations of data mining under Trusted Third Party model. Here we analyze and assess each aspect of this issue, introducing a proposed framework based on strategies of SCP using multilevel encryption and decryption (MLED) algorithm in data mining with respect to the privacy preserving. Though our model consumes more CPU utilisation and execution time but it provides more security than the other cryptographic techniques.