Analyzing the Privacy Preserving Using Big Data Techniques

Recently big data has become a hot research topic. The rising amounts of big data also increase the chance of violate the privacy of individuals. Since big data need high computational power and large storage, distributed systems are used. As multiple parties are concerned in these systems, the risk of privacy violation is improved. There have been a number of privacy-preserving methods developed for privacy protection at different stages (e.g., data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a comprehensive overview of the privacy preservation methods in big data and present the challenge for existing mechanisms. In this paper, we illustrate the infrastructure of big data and the big data life cycle. Furthermore, we discuss the research challenges of the privacy preservation in big data. This paper also presents recent techniques of privacy preserving in big data like hiding a needle in a haystack, identity based anonymization, differential privacy and privacy-preserving big data publishing of big data streams and future research directions related to big data.