Comparative Assessment of Approaches for Security of Big Data Environments

The exponential growth of big data has introduced unprecedented opportunities for innovation and insight, but it has also exposed significant challenges in ensuring data security. This paper presents a comparative assessment of various security approaches tailored for big data environments, including encryption techniques, access control mechanisms, data anonymization, secure multi-party computation, and blockchain-based solutions. Each method is evaluated based on its scalability, computational efficiency, resistance to threats, and adaptability to distributed architectures such as Hadoop and Spark. The study also addresses regulatory and compliance considerations that influence security strategy implementation. By analyzing the strengths and limitations of existing frameworks and technologies, this paper provides a comprehensive perspective to guide stakeholders in selecting appropriate security models. The findings highlight the importance of hybrid approaches and context-aware security models in addressing the dynamic and heterogeneous nature of big data ecosystems.