A Big Data environment is a powerful and complex ecosystem that helps companies extract important information from data to make the best business and strategic decisions. In this context, due to the quantity, variety, and sensitivity of the data managed by these systems, as well as the heterogeneity of the technologies involved, privacy and security especially become crucial issues. However, ensuring these concerns in Big Data environments is not a trivial issue, and it cannot be treated from a partial or isolated perspective. It must be carried out through a holistic approach, starting from the definition of requirements and policies, and being present in any relevant activity of its development and deployment. Therefore, in this paper, we propose a methodological approach for integrating security and privacy in Big Data development based on main standards and common practices. In this way, we have defined a development process for this kind of ecosystems that considers not only security in all the phases of the process but also the inherent characteristics of Big Data. We describe this process through a set of phases that covers all the relevant stages of the development of Big Data environments, which are supported by a customized security reference architecture (SRA) that defines the main components of this kind of systems along with the key concepts of security.

Data is currently one of the most important assets for companies in every field. The continuous growth in the importance and volume of data has created a new problem: it cannot be handled by traditional analysis techniques. This problem was, therefore, solved through the creation of a new paradigm: Big Data. However, Big Data originated new issues related not only to the volume or the variety of the data, but also to data security and privacy. In order to obtain a full perspective of the problem, we decided to carry out an investigation with the objective of highlighting the main issues regarding Big Data security, and also the solutions proposed by the scientific community to solve them. In this paper, we explain the results obtained after applying a systematic mapping study to security in the Big Data ecosystem. It is almost impossible to carry out detailed research into the entire topic of security, and the outcome of this research is, therefore, a big picture of the main problems related to security in a Big Data system, along with the principal solutions to them proposed by the research community.

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Big data ecosystems are increasingly important for the daily activities of any type of company. They are decisive elements in the organization, so any malfunction of this environment can have a great impact on the normal functioning of the company; security is therefore a crucial aspect of this type of ecosystem. When approaching security in big data as an issue, it must be considered not only during the creation and implementation of the big data ecosystem, but also throughout its entire lifecycle, including operation, and especially when managing and responding to incidents that occur. To this end, this paper proposes an incident response process supported by a private blockchain network that allows the recording of the different events and incidents that occur in the big data ecosystem. The use of blockchain enables the security of the stored data to be improved, increasing its immutability and traceability. In addition, the stored records can help manage incidents and anticipate them, thereby minimizing the costs of investigating their causes; that facilitates forensic readiness. This proposal integrates with previous research work, seeking to improve the security of big data by creating a process of secure analysis, design, and implementation, supported by a security reference architecture that serves as a guide in defining the different elements of this type of ecosystem. Moreover, this paper presents a case study in which the proposal is being implemented by using big data and blockchain technologies, such as Apache Spark or Hyperledger Fabric.

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