arXiv 2605.092322026-05-10cs.CRcs.LG

物联网系统中的隐私保护分布式学习：一种统一的威胁模型和评估框架

Privacy-Preserving Distributed Learning in IoT Systems: A Unified Threat Model and Evaluation Framework

Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；Florida Atlantic University（佛罗里达亚特兰大大学）

John Cartmell, Alexander Williams

备注：: 14 pages, 6 figures

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AI总结：

本文提出统一的威胁模型和评估框架，分析物联网分布式学习中的隐私保护技术，揭示隐私强度与系统效率之间的权衡。

摘要：

随着物联网设备的广泛应用，分布式学习框架的使用加速，数据保持本地化而模型更新在去中心化系统间共享。尽管减少了集中式数据收集，但通过梯度、模型参数和中间表示的交换引入了隐私风险。已提出多种隐私保护技术，如差分隐私、加密方法和轻量级系统级方法。然而，现有综述常孤立评估这些方法，缺乏统一框架以在现实攻击模型和物联网资源约束下比较其有效性。本文提出了物联网分布式学习隐私保护技术的结构化分析。引入了统一的威胁模型，涵盖模型逆向、成员推断、梯度泄露和基于通信的攻击。在此基础上，开发了评估框架，从隐私鲁棒性和系统级效率（计算、内存和通信开销）比较方法。使用该框架，分析了代表性方法，包括差分隐私、同态加密、安全多方计算、分布式选择性随机梯度下降和基于Bloom Filter的方法。结果突显隐私强度与系统效率之间的根本权衡。特别是，基于Bloom Filter的编码通过碰撞诱导的模糊性提供轻量级隐私，同时保持低计算和通信开销。本文为物联网系统分布式学习的隐私保护设计提供了统一视角。

英文摘要：

The increasing deployment of Internet-of-Things (IoT) devices has accelerated the use of distributed learning frameworks, where data remains local while model updates are shared across decentralized systems. Although this reduces centralized data collection, it introduces privacy risks through the exchange of gradients, model parameters, and intermediate representations. A variety of privacy-preserving techniques have been proposed to address these risks, including differential privacy, cryptographic methods, and lightweight system-level approaches. However, existing surveys often evaluate these methods in isolation and lack a unified framework for comparing their effectiveness under realistic attack models and IoT resource constraints. This paper presents a structured analysis of privacy-preserving techniques for distributed learning in IoT environments. A unified threat model is introduced that captures model inversion, membership inference, gradient leakage, and communication-based attacks. Building on this model, an evaluation framework is developed to compare methods in terms of both privacy robustness and system-level efficiency, including computational, memory, and communication overhead. Using this framework, representative approaches including differential privacy, homomorphic encryption, secure multi-party computation, distributed selective stochastic gradient descent, and Bloom Filter-based methods are analyzed. The results highlight a fundamental trade-off between privacy strength and system efficiency. In particular, Bloom Filter-based encodings are shown to provide lightweight privacy through collision-induced ambiguity while maintaining low computational and communication overhead. The paper provides a unified perspective on privacy-preserving design choices for distributed learning in IoT systems.

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