Privacy preservation techniques in big data analytics: a survey

Abstract Incredible amounts of data is being generated by various organizations like hospitals, banks, e-commerce, retail and supply chain, etc. by virtue of digital technology. Not only humans but machines also contribute to data in the form of closed circuit television streaming, web site logs, etc. Tons of data is generated every minute by social media and smart phones. The voluminous data generated from the various sources can be processed and analyzed to support decision making. However data analytics is prone to privacy violations. One of the applications of data analytics is recommendation systems which is widely used by ecommerce sites like Amazon, Flip kart for suggesting products to customers based on their buying habits leading to inference attacks. Although data analytics is useful in decision making, it will lead to serious privacy concerns. Hence privacy preserving data analytics became very important. This paper examines various privacy threats, privacy preservation techniques and models with their limitations, also proposes a data lake based modernistic privacy preservation technique to handle privacy preservation in unstructured data