Survey on Privacy Preservation Techniques in Big Data Processing: A Review

Abstract

Big data is a collection of large volume of heterogeneous data. Due to the rapid growth of online social network users, large amount of data is generated every day. Big data processing becomes crucial because of the fast growth of data. Big data includes personal information such as personal identification, salary details, health records etc. As the volume of data increases privacy and security violations may also increase. Privacy refers to the protection of individual’s data. Researchers have developed various privacy preservation techniques. One of the most effective methods for big data privacy is anonymization technique. In this paper we are focusing on different privacy preserving methods such as anonymization, randomization and differential privacy. It also reviewed some merits and demerits of different anonymization techniques such as k-anonymization, l-diversity and t-closeness etc.