A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things

Area of Science:

• Computer Science
• Information Technology
• Cybersecurity

Background:

• The Internet of Things (IoT) is rapidly expanding, connecting numerous devices that handle sensitive personal data.
• Ubiquitous connectivity in IoT environments like smart cities and eHealth necessitates robust data privacy measures.
• Existing privacy solutions are challenged by the scale and nature of data transfer in IoT ecosystems.

Purpose of the Study:

• To provide a comprehensive survey of Machine Learning (ML) and Deep Learning (DL) based privacy solutions for IoT.
• To analyze current privacy threats and attacks targeting IoT devices and data.
• To identify and evaluate the effectiveness of various ML/DL architectures in mitigating these privacy risks.

Main Methods:

• In-depth analysis of prevalent privacy threats and attack vectors in IoT.
• Systematic review of diverse ML and DL architectures applied to IoT privacy.
• Detailed examination of implementations, configurations, and reported results for each ML/DL approach.

Main Results:

• Identification of key privacy vulnerabilities across different IoT applications.
• Categorization of ML/DL techniques based on their suitability for specific privacy threats.
• Empirical data on the performance and efficacy of surveyed ML/DL privacy solutions.

Conclusions:

• Machine Learning and Deep Learning offer promising avenues for enhancing IoT data privacy.
• The choice of ML/DL solution should be tailored to the specific privacy threats and application context.
• Further research is needed to develop more resilient and adaptive privacy-preserving techniques for evolving IoT landscapes.