Privacy Preserving Data Mining
Methods, Execution and Efficiency
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Data mining is under attack from privacy advocates because of a misunderstanding about what it actually is and a valid concern about how it s generally done. This analysis shows how technology from the security community can change data mining for the better, providing all its benefits while still maintaining privacy. Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount ...
Data mining is under attack from privacy advocates because of a misunderstanding about what it actually is and a valid concern about how it s generally done. This analysis shows how technology from the security community can change data mining for the better, providing all its benefits while still maintaining privacy. Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting at the same time sensitive information. Several PPDM techniques have been developed that allow one to hide sensitive item sets or patterns, before the data mining process is executed, such as randomization, k anonymity, data perturbation, secure multiparty computation etc.We mainly analysis two most general & secure approach of PPDM Data Perturbation &Secure Multiparty Computation. Based on the analysis, the solution for PPDM is developed for demonstration. This Analysis should be especially useful to professionals in Cryptography and Data Mining fields.
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