Privacy-Preserving Techniques in UAV Networks: A Systematic Literature Review of Methods, Tools, and Future Directions

In recent years, the extensive use of Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, has raised significant privacy and security concerns, motivating substantial research into Privacy-Preserving Techniques (PPTs). Although many studies have explored this area, existing surveys remain limited in scope, often focusing on specific techniques and rarely offering a clear overview and critical discussion of practical simulation tools. This paper presents a Systematic Literature Review (SLR) of 84 studies to address these gaps. It offers a structured analysis of major PPTs, including Federated Learning (FL), cryptographic methods, blockchain-based approaches, hardware-based solutions, and hybrid approaches. For each technique, the paper examines its core principles and the security and privacy challenges it aims to mitigate, alongside a comparative assessment of strengths and limitations in addressing issues such as communication interception, data leakage, scalability, and resource constraints. In addition, commonly used datasets and simulation tools for evaluating these techniques are reviewed, offering insights into their practical performance. Key challenges are identified, including high computational and energy demands, communication and scalability bottlenecks, data quality limitations, privacy trade-offs, the complexity of managing heterogeneous UAV systems, and the lack of real-world deployment studies across diverse operational scenarios. Finally, clear research directions are outlined, emphasizing the need for lightweight and asynchronous protocols, hybrid privacy-preserving architectures, holistic security frameworks, and, most importantly, improved real-world testing.