A comparative analysis of differential privacy vs other privacy mechanisms for Big Data

Big Data in today's world is being seen as the new opportunity for analytics that can offer considerable achievement to several aspects of our everyday life, including health, environment, employment, etc. The data used in it is not just an available resource but, is constantly being generated by people. And thus Big Data in many cases involves personal data, for example a name, a picture, contact details, posts on social networking websites, healthcare data, location data, computer IP address etc. And due to its extraordinary scale, security and privacy in Big Data faces many challenges. Many new techniques have been suggested and implemented for privacy preservation in big data but unfortunately they seem to be failing due to very nature of big data. In this paper we would be discussing a novel approach for privacy preserving known as differential privacy preserving which is the most suitable mechanism for Big Data alongside the advantages and disadvantages of other available privacy mechanisms.