A Survey on Privacy Preservation Techniques in IoT Systems

Area of Science:

• Computer Science
• Cybersecurity
• Information Technology

Background:

• The Internet of Things (IoT) integrates billions of devices, raising significant privacy and security concerns due to continuous data collection and exchange.
• Applications span smart homes, healthcare, industrial automation, and environmental monitoring, necessitating robust data protection.

Purpose of the Study:

• To systematically review state-of-the-art privacy-preserving techniques in IoT systems.
• To analyze mechanisms protecting user data during collection, transmission, and storage.
• To identify current research trends, limitations, and future directions in secure IoT architectures.

Main Methods:

• Analysis of peer-reviewed studies (2016-2025) and technical reports.
• Examination of applied privacy mechanisms, datasets, and analytical models.
• Focus on decentralized methods (blockchain, federated learning) and emerging techniques (homomorphic encryption, differential privacy).

Main Results:

• Blockchain and federated learning are prevalent decentralized privacy-preserving methods in IoT.
• Homomorphic encryption and differential privacy show promise for lightweight and edge-based IoT.
• Persistent challenges include computational overhead, scalability, and real-time performance in resource-constrained IoT devices.

Conclusions:

• Gaps exist in cross-domain interoperability, energy-efficient cryptography, and privacy solutions for UAV and vehicular IoT.
• Future research should address these limitations to foster secure and privacy-aware IoT architectures.
• Advancements in privacy techniques are crucial for the continued growth and trustworthiness of IoT ecosystems.