PRESERVING PRIVACY USING DATA ANONYMIZATION FOR KNOWLEDGE DISCOVERY
Jalpa Shah Jalpa Shah, Jayshree Upadhyay · Journal of Emerging Technologies and Innovative Research · 2018
In this information age, data and knowledge extracted by data mining techniques represent a key asset driving research, innovation, and policy-making activities. Many agencies and organizations willing to release the data they collected to other parties, for purposes such as research and the formulation of public policies. The success of data mining relies on the availability of high quality data. To ensure quality data mining, effective information sharing between organizations becomes a vital requirement in today's society. Since data mining often involves data that contains personally identifiable information and therefore releasing such data may result in privacy breaches; this is the case for the examples of micro data, e.g., census data and medical data. Privacy preserving data publishing (PPDP) is a study of eliminating privacy threats like linking attack while, at the same time, preserving useful information in the released data for data mining. Privacy Preserving Data Mining (PPDM) field of research studies how knowledge or patterns can be extracted from large data stores while maintaining commercial or legislative privacy constraints. Quite often, these constraints pertain to individuals represented in the data stores. This work is about proposing a method which extends the process of anonymization to achieve new knowledge through data mining while protecting individuals' privacy.