Big Data Security Management

Big Data Security Management

Zaiyong Tang (Salem State University, USA) and Youqin Pan (Salem State University, USA)
DOI: 10.4018/978-1-4666-8505-5.ch003
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Big data is a buzzword today, and security of big data is a big concern. Traditional security standards and technologies cannot scale up to deliver reliable and effective security solutions in the big data environment. This chapter covers big data security management from concepts to real-world issues. By identifying and laying out the major challenges, industry trends, legal and regulatory environments, security principles, security management frameworks, security maturity model, big data analytics in solving security problems, current research results, and future research issues, this chapter provides researchers and practitioners with a timely reference and guidance in securing big data processing, management, and applications.
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Big Data is generally considered to have three defining characteristics: volume, variety and velocity (Zikopoulos, et al. 2012). When at least one of the dimensions is significantly high, the data is labeled big. Traditional techniques and technologies are not sufficient to handle big data. With the enormous size, speed, and/or multiplicity, big data processing requires a set of new forms of technologies and approaches to achieve effective decision support, insight discovery, and process optimization (Lancy, 2001). Although the three V’s of big data definition has wide acceptance, recently, there have been attempts to expand the dimension of big data to include value and veracity (Demchenko, et al., 2013). The value dimension of big data deals with drawing inferences and testing hypothesis, and the veracity dimension is about authenticity, accountability, availability, and trustworthiness. Some researchers (e.g., Biehn, 2013) have suggested adding value and viability to the three V’s.

Although big data has been discussed for over a decade since 2000, interest in big data has only experienced significant growth in the last few years. Figure 1 shows the Google search interest for the search term “Big Data” from January 2004 to June 2014. The figure does not show actual search volume. The y axis represents search interest relative to the highest point, with the highest point being scaled to100. For more historical information about big data, the reader is referred to Press (2013), which documents the history of big data that dates back to the 1940s.

Figure 1.

Big Data Web Search Interest, January 2004 – June 2014

Source: Google Trends

Key Terms in this Chapter

Privacy: An individual’s right to safeguard personal information in accordance with law and regulations.

Security Attack: An attempt to gain unauthorized access to information resource or services, or to cause harm or damage to information systems.

Big Data Security Maturity Model: A multi-level model used to help organizations to articulate where they stand in the spectrum of big date security, from nonexistent to optimality.

Big Data: Unstructured date with five characteristics: volume, velocity, variety, veracity, and value.

Big Data Security: The protection of big data from unauthorized access and ensure big data confidentiality, integrity, and availability.

Big Data Security Framework: A framework designed to help organizations to identify, assess, control, and manage big data security and maintain regulatory compliance.

Security: A state of preparedness against threats to the integrity of the organization and its information resources.

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