Design and Development of Privacy Preserving Techniques for Data Stream Mining

Abstract

diverse application areas such as healthcare, banking and financial, telecommunication, newlineshopping records, personal data and so on. These applications frequently newlineproduce huge volume of data which is stored statically and dynamically in the newlineavailable network. The mined statistics can be in the form of clusters, patterns, newlinerules and classification. Distribution of such data is demonstrated to be advantageous newlinefor data mining application. This dataset frequently encompasses classifiable newlineinformation individually and consequently freeing such data may result in newlineprivacy breaches. Preserving privacy while delivering data is a fundamental study newlinearea in data security and also it is a major issue in delivering individual exact sensitive newlineinformation. Efficient preservation of data proprietor s privacy is a crucial newlineissue while broadcasting the data for analysis purpose. As per our knowledge, newlinedataset is an essential asset for industry in order to take a decision by examining newlineit. In order to distribute the data along side preserving privacy, the data proprietor newlinemust come up with a result which accomplishes the double goal of privacy newlinepreservation as well as accuracy of data mining task, mostly clustering and classification. newlineData mining can be valuable in many applications, but due to insufficient newlineprotection the data may be abused for other goals. It is essential to prevent newlinerevealing of not only the individual confidential information but also the critical newlineknowledge. Generally, data proprietors do not find it safe to publish datasets for newlinemining purpose because of their worry that releasing of data may compromise newlinean individual s private information. Perturbation and Anonymizing datasets before newlinereleasing overcomes such a fear as it guarantees secrecy of personal information. newlineBut, protecting personal information and achieving mining results as close newlineas that of with original datasets poses great challenges. The Proposed research newline