Security challenges in big data analytics
Kaushal Kishor Singh, Sunil Kumar Gautam · 2024
The introduction of Big Data analytics has completely changed how businesses get insights from enormous and varied information. However, as data volume, velocity, and diversity increase, so do the security issues related to its processing, storage, and analysis. This chapter gives a general overview of the major security issues that businesses using big data analytics must deal with. The article starts by talking about the problem of data leaks and unauthorized access. The security and integrity of data have become a top priority as large datasets are being stored in remote locations. Data breaches, identity theft, and severe financial losses may all result from unauthorized access. To reduce these risks, it is essential to have reliable authentication and access control systems. The chapter also examines the dangers presented by insider threats. Those with privileged access to big data repositories, such as employees or contractors, may purposefully or unintentionally breach data security. To create accountability and identify internal risks, effective monitoring and auditing are necessary. This chapter's third difficulty is related to data encryption. Big Data's immense scale often makes the usage of encryption methods for data in transit and at rest necessary. It may be difficult to scale up encryption without sacrificing performance, however. In Big Data analytics, finding the ideal balance between security and performance is crucial. The study then addresses the topic of data lineage and provenance. To ensure that data is reliable, it is essential to understand its origin and history. Big Data settings sometimes do not have thorough systems for monitoring data history, which makes it difficult to find data manipulation or inaccuracies. The chapter also emphasizes the difficulty of adhering to data privacy laws like GDPR and CCPA. Big Data analytics companies must traverse a complicated web of legal and regulatory regulations, which might differ across nations. A never-ending difficulty is ensuring compliance while gaining useful insights from data. The chapter also discusses how adversarial assaults are a growing danger for machine learning models employed in Big Data analytics. Attackers have the ability to alter input data to trick machine learning systems, producing false results. An active study topic in the discipline is creating resilient models that can withstand such assaults. In conclusion, this chapter offers a summary of the many security issues that Big Data analytics raises. Addressing these issues is crucial to preserve data, uphold trust, and guarantee compliance with changing data protection requirements as firms continue to use big data to gain a competitive advantage. It is crucial to have comprehensive security plans that include people, systems, and data in order to reduce the dangers related to Big Data analytics.