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Preserving Private Data

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Data is often far from perfect. Most of the data mining technique can tolerate and bare some level of imperfection and inconsistency in the data. This imperfection and inconsistency may cause loss of individual’s private data. Preserving privacy data mining focuses on obtaining positive result analysis. The data quality issues that often need to be addressed includes the presence of noise and outliers, missing inconsistent or duplicate data, biased and unrepresentative phenomenon and that the data is supposed to be. For preprocessing and pattern recognition it is mandatory to build the data in representable form before providing it to data mining techniques. In this paper we introduce Noise cure framework as a bridge between data preprocessing and data mining for preprocessing the raw data to bring in a form suitable for pattern recognition in preserving privacy data mining
Keywords: Privacy, outliers, duplicate, noise, imperfection, inconsistency, quality analysis.

Introduction
Preserving private data focuses on a very and first private data of individuals. For eg: Even though a transaction auditor in any credit card company or other organization try to customer from fraud and pay special attention from card usage that are rather different from typical cases , the threats may occur to individuals private data. [7]. Noisy data is meaningless data or corrupt data-any data that cannot be understood and interpreted correctly by machine such as unstructured text. In
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