Towards an Understanding of Privacy Management Architecture in Big Data: An Experimental Research

Big data analytics provide valuable information allowing organizations to gain insights that grant them a competitive advantage in the market. However, it also provides access to data that compromise people's privacy. The development of sophisticated technologies for data analysis has resulted in a growing concern around privacy management in big data. While many sites (e.g. Facebook) require the user to provide personal information to access their services, others (e.g. Google search) can automatically capture or trace user activities and use that data to acquire personal demographic information. Therefore, Internet users are - willingly or unwillingly - constantly disclosing sensitive personal information. In addition, users do not get a complete picture of how their personal information is disseminated online. In this paper, we investigate information privacy through an experiment using large-scale disclosure of personal web activity data to track fragments of personal information released over a period of time. This experiment gives a clear picture of the potential privacy losses of individual users based on released personal information and activities at different websites. By devising an enterprise architecture using a privacy-by-design framework, this study provides a useful guide to addressing the managerial challenges of privacy management.