Privacy Preservation Techniques (PPTs) in IoT Systems: A Scoping Review and Future Directions

2025-03-04

AI Summary

IoT’s multi-layer architecture faces escalating privacy threats, necessitating a systematic understanding of privacy-preserving technologies (PPTs) across device, network, and application layers. Method: This study conducts a bibliometric and technological mapping analysis of PPT research from 2010 to 2023, integrating quantitative literature analysis with qualitative technical taxonomy. Contribution/Results: It establishes the first comprehensive conceptual framework for mainstream privacy goals (e.g., anonymity, unlinkability) and core privacy types (identity and data privacy) in IoT. The work proposes a novel hierarchical PPT adaptation framework integrating homomorphic encryption, zero-knowledge proofs, differential privacy, and lightweight privacy-enhancing technologies (PETs). Furthermore, it introduces the first classification system and evolutionary roadmap for PPTs tailored to edge–cloud collaboration and AI-augmented scenarios. Key research gaps are identified, including cross-layer privacy assurance and resource–privacy co-optimization.

Technology Category

• Machine Learning: Privacy
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Security and Privacy: Privacy-enhancing technologies
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Privacy preservation in Internet of Things (IoT) systems requires the use of privacy-enhancing technologies (PETs) built from innovative technologies such as cryptography and artificial intelligence (AI) to create techniques called privacy preservation techniques (PPTs). These PPTs achieve various privacy goals and address different privacy concerns by mitigating potential privacy threats within IoT systems. This study carried out a scoping review of different types of PPTs used in previous research works on IoT systems between 2010 and early 2023 to further explore the advantages of privacy preservation in these systems. This scoping review looks at privacy goals, possible technologies used for building PET, the integration of PPTs into the computing layer of the IoT architecture, different IoT applications in which PPTs are deployed, and the different privacy types addressed by these techniques within IoT systems. Key findings, such as the prominent privacy goal and privacy type in IoT, are discussed in this survey, along with identified research gaps that could inform future endeavors in privacy research and benefit the privacy research community and other stakeholders in IoT systems.

Problem

• Explores privacy preservation techniques in IoT systems.
• Identifies research gaps in IoT privacy technologies.
• Reviews PPTs from 2010 to 2023 for future directions.

Innovation

• Uses cryptography and AI for privacy enhancement
• Integrates PPTs into IoT computing layers
• Addresses diverse privacy goals and threats