Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap

2026-03-13

AI Summary

This work addresses the severe privacy risks faced by resource-constrained heterogeneous devices in the Internet of Things (IoT) across the entire pipeline of sensing, communication, and distributed training, where traditional centralized approaches are ill-suited. It proposes the first unified cross-paradigm framework for privacy-preserving machine learning tailored to IoT, systematically integrating techniques such as differential privacy, federated learning, cryptographic methods (including homomorphic encryption and secure multi-party computation), and generative models. The study rigorously analyzes the multidimensional trade-offs among privacy guarantees, computational and communication overhead, scalability, and adversarial robustness. It comprehensively summarizes the balance between privacy, efficiency, and accuracy in existing approaches, catalogs representative datasets, open-source tools, and evaluation benchmarks, and outlines promising future directions—including hybrid privacy mechanisms, energy-aware learning, and privacy-preserving large language models—to provide a systematic roadmap for next-generation mobile intelligent systems.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Bias, Fairness & Privacy
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

The rapid proliferation of the Internet of Things has intensified demand for robust privacy-preserving machine learning mechanisms to safeguard sensitive data generated by large-scale, heterogeneous, and resource-constrained devices. Unlike centralized environments, IoT ecosystems are inherently decentralized, bandwidth-limited, and latency-sensitive, exposing privacy risks across sensing, communication, and distributed training pipelines. These characteristics render conventional anonymization and centralized protection strategies insufficient for practical deployments. This survey presents a comprehensive IoT-centric, cross-paradigm analysis of privacy-preserving machine learning. We introduce a structured taxonomy spanning perturbation-based mechanisms such as differential privacy, distributed paradigms such as federated learning, cryptographic approaches including homomorphic encryption and secure multiparty computation, and generative synthesis techniques based on generative adversarial networks. For each paradigm, we examine formal privacy guarantees, computational and communication complexity, scalability under heterogeneous device participation, and resilience against threats including membership inference, model inversion, gradient leakage, and adversarial manipulation. We further analyze deployment constraints in wireless IoT environments, highlighting trade-offs between privacy, communication overhead, model convergence, and system efficiency within next-generation mobile architectures. We also consolidate evaluation methodologies, summarize representative datasets and open-source frameworks, and identify open challenges including hybrid privacy integration, energy-aware learning, privacy-preserving large language models, and quantum-resilient machine learning.

Problem

• Privacy-Preserving Machine Learning
• Internet of Things
• Data Privacy
• Distributed Learning
• Resource-Constrained Devices

Innovation

• privacy-preserving machine learning
• Internet of Things
• federated learning
• differential privacy
• secure multiparty computation