Data Renovation Algorithm for Protecting Sensitive Categorical Data
G. S. Karthick · International Journal for Research in Applied Science and Engineering Technology · 2017
In the fast growing technological world, improvements and automation of various kinds of fields leads to increasing in growth of data. Such collection of data can be used frequently by economists, statisticians, scientists, forecasters, communication engineers, business and health care predictions in order to take any decision based on the historical factors. At one side data mining becoming the forefront of many fields, on the other side privacy risk factors also tremendously huge. Data given to the users like researchers or to any other third parties may contain sensitive data about an individual that must be protected from breach. In recent periods, many techniques were proposed and developed by the researchers to preserve the privacy in data. This proposed Data Renovation Algorithm (DRA) particularly paying attention on protecting sensitive categorical attributes in a dataset. It alters the original sensitive categorical attribute values and produce modified dataset. Both the original and modified datasets are applied to data mining techniques individually and the results produced for both datasets are equal.