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Privacy preserving in data mining technical: A review Kumaran U*, Khare Neelu, Suraj A Sai School of Information Technology and Engineering, VIT University, Vellore, Tamilnadu, India *Corresponding Author E-mail: kumaran.u@vit.ac.in
Online published on 2 March, 2017. Abstract Data mining produces a large amount of data that needs to be analyzed and prioritized in order to extract useful information from it and gain more knowledge from the data. The aim of data mining tools is to find useful patterns, techniques and models from the available of large data. Hence knowledge about various data mining techniques may contain private information about people or business. The data in data mining is vulnerable to data hackers and employees to take advantage of the situation and misuse data. Preservation of privacy is a significant aspect of data mining and as secrecy of sensitive information must be maintained while sharing the data among different un-trusted parties. To protect the privacy of sensitive data without losing the usability of data, various techniques have been used in privacy preserving data mining (PPDM) to achieve the goal. The aim of this paper is to present privacy preserving data mining techniques. Current application systems are suffering several data privacy during online. There is required some work for content privacy on web. This work plans to work on privacy of web content during data extraction and clustering. Top Keywords Content mining, privacy persevering, data extraction, clustering, web mining. Top | |
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