Abstract
The value of Big Data is now being recognized by many industries and governments. The efficient mining of Big Data enables to improve the competitive advantage of companies and to add value for many social and economic sectors. In fact, important projects with huge investments were launched by several governments to extract the maximum benefit from Big Data. The private sector has also deployed important efforts to maximize profits and optimize resources. However, Big Data sharing brings new information security and privacy issues. Traditional technologies and methods are no longer appropriate and lack of performance when applied in Big Data context. This chapter presents Big Data security challenges and a state of the art in methods, mechanisms and solutions used to protect data-intensive information systems.
TopSecurity Challenges In Big Data Context
As mentioned by (Kim, Kim, & Chung, 2013), security in Big Data context includes three main aspects: information security, security monitoring and data security. For (Lu et al., 2013), managing security in a distributed environment means to ensure Big Data management, system integrity and cyberspace security.
Generally, Big Data security aims to ensure a real-time monitoring to detect vulnerabilities, security threats and abnormal behaviours; a granular role-based access control; a robust protection of confidential information and a generation of security performance indicators. It supports rapid decision-making in a security incident case. The following sections identify and explain a number of challenges to achieve these goals.
Big Data Nature
Because of Big Data velocity and huge volumes, it is difficult to protect all data. Indeed, adding security layers may slow system performances and affect dynamic analysis. Thus, access control and data protection are two “BIG” security problems (Kim et al., 2013). Furthermore, it is difficult to handle data classification and management of large digital disparate sources. Even though that the cost by GB has diminished, Big Data security requires important investments. In addition to all that, Big Data is most of the time stored and transferred across multiple Clouds and distributed worldwide systems. Sharing data over many networks increase security risks.
Key Terms in this Chapter
Encryption: Encryption relies on the use of encryption algorithms to transform data into encrypted forms. The purpose is to make them unreadable to those who do not possess the encryption key(s).
Big Data: Big Data is mainly defined by its 3Vs fundamental characteristics. The 3Vs include Velocity (data are growing and changing in a rapid way), Variety (data come in different and multiple formats) and Volume (huge amount of data is generated every second).
Security Management: Security management is a part of the overall management system of an organization. It aims to handle, implement, monitor, maintain, and enhance data security.
Privacy: Privacy is the ability of individuals to seclude information about themselves. In other words, they selectively control its dissemination.
Confidentiality: Confidentiality is a property that ensures that data is not made disclosed to unauthorized persons. It enforces predefined rules while accessing the protected data.
Anonymization: Anonymization is the process of protecting data privacy across information systems. Several models and methods are used to implement it such as: t-closeness, m-invariance, k-anonymity and l-diversity.
Authentication: Authentication aims to test and ensure, with a certain probability, that particular data are authentic, i.e., they have not been changed.