Privacy-Preserving Data Mining for Relational Key Logger Using Multi-party Knowledge Discovery Process
Achieving data security and privacy-preserving system efficiency in the data mining and transmission process is great significance and challenging for security problems because the relational role access of data is sensitive and in enormous amount be processed. A classic instance of a privacy-preserving data mining badly-behaved of the first type happens in the field of privacy research. Data Mining and Information Detection in Databases are two new capacities of database knowledge that inspect the involuntary removal for identifying hidden decorations and privacy role for authentication. In this paper, we propose a privacy-preserving data mining key role algorithms are fundamentally based on crypto study for efficient relational privacy process to improve the data mining security process. After this, successively present classification rule mining algorithms on these data the data-mining constituent of the KDD process often the identification of relevant multiparty constraints are frequently identified to provide the key-logger system to access the authentication process. The relation analysis presents an indication of the key objectives of data mining, a report of the approaches used to address these goals of security produce high performance compared to the existing system.
Author Name: M. Karthika
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Additional Author : Dr.S. Kumaravel
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College: Mahendra Arts & Science College, Kalippatti, Namakkal (Dt), Tamilnadu.
Keywords: Data Mining, Sensitive Information, Privacy-Preserving Data Mining, Crypto Analyzer, key-logger, Knowledge Discovery from Data Process (KDD).