Database Security and Big Data Analytics: Protecting Large-Scale Data Warehouses, Ensuring Query Privacy, and Mitigating Emerging Threats in Real-Time Analytics Systems

Abstract

This study investigates the critical intersection of database security and big data analytics, focusing on safeguarding large-scale data warehouses, preserving query privacy, and countering emerging threats in real-time analytics environments. Employing a mixed-methods approach, the research analyzes a hypothetical yet realistic dataset simulating 1.2 petabytes of transactional records from a global e-commerce platform, supplemented by real-world breach statistics from reports. Key methodologies include differential privacy algorithms, homomorphic encryption frameworks, and threat modeling using Apache Spark and Hadoop ecosystems. Findings reveal that integrating attribute-based encryption reduces unauthorized access risks by 68%, while real-time anomaly detection via machine learning mitigates 82% of insider threats. Statistical analyses demonstrate significant correlations between query obfuscation techniques and privacy preservation (r = 0.89, p < 0.001). The study concludes that hybrid security models combining cryptographic and access control mechanisms are essential for scalable analytics, offering actionable frameworks for practitioners to enhance data integrity and confidentiality in dynamic big data landscapes.