Data Behind the Walls An Advanced Architecture for Data Privacy Management

In today's highly connected society, we are constantly asked to provide personal information to retailers, voter surveys, medical professionals, and other data collection efforts. The collected data is stored in large data warehouses. Organisations and statistical agencies share and use this data to facilitate research in public health, economics, sociology, etc. However, this data contains sensitive information about individuals, which can result in identity theft, financial loss, stress and depression, embarrassment, abuse, etc. Therefore, one must ensure rigorous management of individuals' privacy. We propose, an advanced data privacy management architecture composed of three layers. The data management layer consists of de-identification and anonymisation, the access management layer for re-enforcing data access based on the concepts of Role-Based Access Control and the Chinese Wall Security Policy, and the roles layer for regulating different users. The proposed system architecture is validated on healthcare datasets.Comment: 7 page

Big Data Storage Tools Using NoSQL Databases and Their Applications in Various Domains: A Systematic Review

Over the past few years, data has been growing significantly due to the advent of new connected devices, availability of bandwidth, and the emergence of new applications which utilize cloud computing infrastructure in the data centers. This increased amount of data faces many problems in terms of storage, transmission, management, and processing, etc. Therefore, the term big data has gained significant attention from researchers in recent years. The rapidly growing quantity, velocity, and variety of data require more probable and logical tools for its storage. For this purpose, the industry is highly emphasizing the development of more viable tools for the storage of big data. The traditional big data storage tools are unsuccessful in storing an enormous amount of data. Hence, the structural modifications of management mechanisms of conventional storage systems such as SQL databases to NoSQL databases technology are necessary to cope up with drastically increasing requirements of big data storage. The primary objective of this paper is to concentrate exclusively on designing a road map for NoSQL big data storage technologies, evaluate current evidence, research progresses in NoSQL data storage systems and their applications in various domains. We conducted a systematic literature review (SLR) of various studies published in recent years. We propose a framework to classify selected articles on the basis of various factors such as motivations behind big data storage, NoSQL techniques used for storing big data, and significant applications of big data in different domains. Furthermore, we also discuss research issues and define an outline for future research in the big data storage domain for NoSQL databases