PRESERVING PRIVACY DURING BIG DATA PUBLISHING USING K-ANONYMITY MODEL – A SURVEY

Abstract

The advancements in technology have led to a massive increase in the amount of data that is generated now a days. This huge amount of data that is generated from multiple sources is known as big data. This big data is used for analysis by Businesses, Healthcare Organizations, Government, etc. As big data also contains user-specific information, directly releasing this data for analysis can pose serious threats to user’s privacy. So to preserve privacy in big data publishing, Anonymization techniques are used. Anonymization use generalization and suppression techniques to preserve privacy of an individual. There are many privacy models, like k-anonymity, l-diversity, t-closeness, that are used to anonymize the data. There are many algorithms that are used to implement k-anonymity model. This survey paper will first explain privacy models that are used to anonymize the data and then gives an overview of the various algorithms that are used to implement k-anonymity.