Innovative Perturbative Masking Techniques for DTT and MDTT in Privacy Preserving Data Mining

Innovative Perturbative Masking Techniques for DTT and MDTT in Privacy Preserving Data Mining

Data mining is defined in many ways in different situations. Major definitions used are: It refers to the finding of relevant and useful information from data base [1]. It involves the use of data analysis tools to discover previously unknown, valid patterns and relationships in large data sets

Privacy preserving data mining has gained increasing popularity in the data mining research community. Privacy preserving data mining has become increasingly popular because it allows sharing of privacy-sensitive data for analysis purposes.

The goal of these privacy preserving methodologies is to ensure that the sanitized dataset

(a) Properly shields all the sensitive information that was contained in the original dataset (b) has properties similar to the original dataset, e.g., first/second order statistics, etc., possibly resembling it to a high extent.

(c) Maintains reasonably accurate data mining results when compared to those attained when mining the original dataset.

Inference control in statistical databases, also known as Statistical Disclosure Control (SDC) or Statistical Disclosure Limitation (SDL), seeks to protect statistical data in such a way that they can be publicly released and mined without giving away private information that can be linked to specific individuals or entities., Since data protection ultimately means data modification, the challenge for SDC is to achieve protection with minimum loss of the accuracy sought by database users.SDC methods for protecting individual data (micro data)

A number of methods have recently been proposed for privacy preserving data mining. Thus from the result it shows that, in data mining and in Statistical calculation

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