Demystifying Big Data in the Cloud: Enhancing Privacy and Security Using Data Mining Techniques

Introduction

Computing has become ubiquitous, creating countless new digital puddles, lakes, and oceans of data in its scale, time, type, characteristics, nature …, which is big data in the cloud. Big data is a newly emerging technology and promising to handle this data deluge. However, it also has a big challenge and creates possible risks for the organizations and consumers or users’ data security and privacy. “In big data no secret” –if so how does personal or some other sensitive data secure?’ Therefore, demystifying big data in the cloud using a Data Mining (DM) approach is a systematic approach to overcoming such potential problems and optimization of cloud performance. Enhancing Cloud Data Privacy and Security (ECDPS) based on DM techniques or technologies are fueling the use of inferential data techniques, for example, spatial data, traffic monitors, magnetic resonance imaging machines, and biological and chemical sensors monitoring … namely few. Moreover, big data as a technology is expected to play a big role to transform competitive dynamics in business and science from root level to the macro level from retail to biotech in tremendously. DM techniques integrated with agents’ technologies of searching and/or mining processes for implicit, previously unknown, and potentially valuable information from large-scale data sets are a tactical approach of big data, which support to detect cloud resources risks, such as intrusion patterns, security problems, and so on (Bhavani, Latifur, Mohammad & Kevin, 2008; Lin, Hinke & Marks, 1996).

Security and privacy in the cloud are essential and core components. For example, for consumer's trust and involvement are determined by these factors, such as expanding their online profile and storing personal information in the cloud, which sets up payments and billing for cloud software services, shopping and other activities (Chris & Don, 1996). However, big data is a growing technology that did not well adopt to handle, manage and analytics of massive data that generated by various sources. In such federated and massive data exploration tasks, privacy and security have always been a barrier to adoption. Therefore, understanding big data is the first step in assessing the potential risks need and putting a big data analytics by enhancing DM based security and privacy. The second understands the cloud system and its trends that are affecting organizations looking forward to derive business value, and competitive advantages, from increasingly large and diverse data sets. In this proposed research, the detail of big data in the cloud, DM techniques towards cloud data security and privacy are discussed, which would be a generic inferring paper for scientists, researchers, students and decision makers in general.

In this chapter, authors introduce a novel idea and methodology using DM techniques. It is a systematic approach to demystifying implications of big data in the cloud by knowing and expanding the inferential data analysis. It is a paradigm of empowering the widely adoptions of the new and dynamic data analysis and systems namely DM, including Machine Learning (ML), Artificial Intelligence (AI), Neural Networking (NN) and others (Jainendra, 2014). The inference is the process of establishing relationships between data sets, the same objective as DM techniques. That is, given definite attributes apply to a set of data, authors “know,” which certain other attributes also apply to that set of data. That is equivalent to stating the one set “implies” the other. Systematically, DM is a process of an attempt to answer the long-standing cloud security and privacy question “what does all the cloud data mean? Or free access and sensitivity of such data sets, which is inherently an attempt to automate the inference problem in the cloud security.