Proposed System for PPDDM using Enhanced Algorithms
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In recent years, increased concern over personal information and privacy protection has led to the development of a number of privacy protection techniques. These techniques have been suggested to ensure that data mining can be performed while the preservation of private information protection is maintained. In this book, a new system for privacy preserving distributed data mining (PPDDM) of association rules is proposed. This proposed system works under the common and realistic assumptions that parties are semi-honest, Semi-Trusted Third Party (STTP) and the databases are horizontally distrib...
In recent years, increased concern over personal information and privacy protection has led to the development of a number of privacy protection techniques. These techniques have been suggested to ensure that data mining can be performed while the preservation of private information protection is maintained. In this book, a new system for privacy preserving distributed data mining (PPDDM) of association rules is proposed. This proposed system works under the common and realistic assumptions that parties are semi-honest, Semi-Trusted Third Party (STTP) and the databases are horizontally distributed over these parties. The system supports two levels for privacy; one for hiding sensitive rules and another for evaluating global associations rules for the remaining rules over different data parities without revealing any private data for any party. The proposed system can be used in any distributed database environment which needs to extract knowledge like hospitals, banks, telecommunication companies or any other organizations.
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