A Comprehensive Evaluation of Privacy-Preserving Mechanisms in Cloud-Based Big Data Analytics: Challenges and Future Research Directions

The paradigm shift toward cloud-based big data analytics has empowered organizations to derive actionable insights from massive datasets through scalable, on-demand computational resources. However, the migration of sensitive data to third-party cloud environments introduces profound privacy concerns, ranging from unauthorized data access to the risk of re-identification in multi-tenant architectures. This paper provides a comprehensive evaluation of current Privacy-Preserving Mechanisms (PPMs), systematically analyzing their efficacy in safeguarding data throughout its lifecycle—at rest, in transit, and during computation. The evaluation covers a broad spectrum of Privacy-Enhancing Technologies (PETs), including Differential Privacy (DP), Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC), and Trusted Execution Environments (TEEs). We examine the inherent trade-offs between data utility and privacy protection, specifically addressing the “utility-privacy” bottleneck where high levels of noise injection or encryption complexity often degrade the accuracy and performance of analytical models. Furthermore, the study explores the integration of Federated Learning as a decentralized approach to privacy, allowing for collaborative model training without the need for raw data movement. Critical challenges are identified, such as the scalability of cryptographic protocols in high-volume data streams and the regulatory pressures imposed by global standards like the GDPR and the EU AI Act. By synthesizing current industry practices with academic research, this paper highlights the gap between theoretical privacy models and their practical implementation in production-grade cloud infrastructures. The discourse concludes with a strategic roadmap for future research, emphasizing the need for Post-Quantum Cryptography (PQC) and automated privacy-orchestration frameworks. This comprehensive review serves as a foundational reference for researchers and system architects aiming to design resilient, privacy-centric cloud analytical systems that maintain compliance without sacrificing computational efficiency.