Data Anonymity - Privacy preservation in Open Data Publishing

Over the last few years with the exponential increase in the amount digital data generated from and stored in large warehouses for various scientific and statistical analytical-scientific purposes. There is growing concern about the data privacy as the data collected may contain personal sensitive information as well other crucial information. Hence there is a probability that this could be misused. In our study we highlight the key concepts, tools and techniques in data privacy protection, a vital component that forms the life cycle of digital data, starting from data gathering - data processing ,data analysis to visualization and interpretation. We apply data anonymity, a commonly used data protection technique which works on k anonymity algorithm and study the resultant outcome. Data anonymity and other data protection techniques seldom come with information loss, which may alter the overall analysis result bychanging the nature of dataset. In this study we apply and anonymize the dataset using different techniques and study the outcomes.

Autorentext
Mr K. V. Vittal Hebbar has obtained his master degree in data science from university of East London ,London in Sep 2015.Key programs included in data science are Data Ecology ,qualitative,quantitative data analysis ,advanced decision making. Mr Hebbar has experience in the space of analytic-programming and working on a project for a global firm.