SECURING PRIVACY IN DATA MINING: A SURVEY OF TECHNIQUES
Tanzeela Javid, Manoj Gupta, Abhishek Gupta · Journal of Natural Remedies · 2020
Since two decenniums the expansion of dominant data mining tools and their use on data available over the internet in electronic form has posed threats to individual’s privacy and data security. It is believed that legitimate privacy involvement is with spontaneous connection to an individual’s records, especially connection to keen information. Awkward or devoid discovery control can be the basic reason of privacy controversies. Methods that involve securing data privacy while increasing the data usefulness are termed as privacy-enhanced data mining (PEDM) methods. To deal with privacy troubles, plentiful data security-intensifying techniques have been worked upon. In this survey, a scheme of classification for privacy-enhanced data mining techniques is given which categorizes various techniques according to the data distribution. This classification scheme divides the privacy-enhanced data mining techniques into two groups; centralized privacy-enhanced data mining techniques and distributed privacy-enhanced data mining techniques. Thus based on the data distribution user can apply the technique according to the need of application.