A Systematic Review of Privacy in Recommendation Engines: Verification, Optimization, and Scalable Computing Perspectives

Abstract

Recommendation engines have become integral to modern digital platforms, enabling personalized services in domains such as e-commerce, healthcare, and social media. However, the increasing reliance on user data has raised significant privacy concerns, including data leakage, inference attacks, and unauthorized profiling. This paper presents a systematic review of privacy-preserving techniques in recommendation systems, focusing on verification methods, optimization strategies, and scalable computing approaches. A total of 30 studies published between 2018 and 2023 are analyzed, covering key privacy models such as differential privacy, federated learning, homomorphic encryption, and secure multi-party computation. The review highlights the trade-off between privacy and recommendation accuracy, which remains a central challenge in the field. For instance, differential privacy introduces noise to protect user data but may degrade recommendation quality .

The paper also examines verification techniques for ensuring privacy guarantees, optimization approaches for improving computational efficiency, and scalable architectures such as distributed and edge-based recommendation systems. Emerging trends, including graph neural networks and cross-domain recommendation frameworks, are discussed in the context of privacy preservation. The findings indicate that while significant progress has been made, challenges such as scalability, fairness, and dynamic privacy adaptation remain open. The paper concludes with future research directions focusing on hybrid privacy models, AI-driven optimization, and privacy-aware system design.