Using Data Anonymization in big data analytics security and privacy

Abstract

Big Data and Analytics mean an enormous and complex collection of very diverse information, which is processed with various technologies and methods to produce and deliver useful and valuable insights. Analytics is the science of using data, or information to extract useful and actionable insights, facts and knowledge from a collection of data it could be stated that Big Data Analytics is the best thing since every commercial data system ever built, although everybody with a more optimistic vision of technology would like to take note that there is a fine line where Everything Data crosses the boundary to something else, especially with regard to privacy and security of the world as we know it. Privacy and security are two distinct but closely related phenomena. Whereas privacy refers to the control over access to the individual, security refers to the stability or strength of controls designed to protect the individual’s privacy. There are many obvious considerations and obstacles when attempting to securely share data. During big data analytics, many invasive techniques such as data fusion, cross-correlation, and algorithm training are often conducted over shared data, which can lead to severe privacy leaks. This means that every enterprise, organization, and individual maintaining large data repositories are in danger of being breached. Our study teaches us that security, privacy, and ethical concerns in big data analytics do not exist in parallel to the business cycle, but must be wisely and ethically managed in coherence throughout all emerging processes of the big data and information systems.