Incremental Detection of Inconsistencies in Distributed Data

Product Discription

In Mining knowledge discovery plays an important role for investigating raw data. Different researchers are working on this where new patterns and unknown information can be extract from large set of data. So data collection, dataset elimination are new field of research for maintain the data privacy with consistency. Here this is required because current data mining algorithms are so intelligent that information can be easily removed, learn or modify. So this kind of study or work comes under privacy preserving mining. It is well documented that this new without limits explosion of new information through the Internet and other media, has reached to a point where threats against the privacy are very common on a daily basis and they deserve serious thinking. Privacy preserving data mining [9, 8], is a novel research direction in data some way, so that the private data and private knowledge remain private even after the mining process. The problem that arises when confidential information can be derived from released data by unauthorized users is also commonly called the “database inference” problem. In this explosion, we provide a classification and an extended description of the various techniques and methodologies that have been developed in the area of privacy preserving data mining. The problem of privacy preserving data mining has become more important in recent years because of the increasing ability to store personal data about users and the increasing sophistication of data mining algorithm to leverage this information.

Whole work is divide in two module first is site architecture building then data distribution as per proposed architecture. Here as per the relation between the data items distribution of data is done. Before transferring the data to the site encryption is performed. Explanation of whole work is shown in fig. 

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