基于联邦学习和区块链技术的TAP规则处理系统

doi:10.3969/j.issn.1671-1122.2024.03.012

摘要/Abstract

摘要：

触发执行编程（Trigger-Action Programming，TAP）为用户联动物联网（Internet of Things，IoT）设备提供了便捷的编程范式。利用机器学习对用户已编辑的TAP规则进行分析，实现TAP规则推荐和生成等功能可以提升用户体验。但TAP规则可能包含个人隐私信息，用户对上传和分享TAP信息存在顾虑。文章提出了基于联邦学习和区块链技术的TAP规则处理系统，用户可在本地进行TAP模型训练，无需上传隐私数据。为解决集中式服务器单点故障和防范恶意模型参数上传的问题，文章利用区块链技术改进集中式TAP联邦学习架构。用户将本地模型更新的累积梯度传输给区块链中的矿工，进行异常识别和交叉验证。矿工委员会整合正常用户提供的累积梯度，得到的全局模型作为一个新区块的数据，链接到区块链上，供用户下载使用。文章采用轻量级无监督的非负矩阵分解方法验证了提出的基于联邦学习和区块链的分布式学习架构的有效性。实验证明该联邦学习架构能有效保护TAP数据中的隐私，并且区块链中的矿工能够很好地识别恶意模型参数，确保了模型的稳定性。

关键词: 触发执行编程, 非负矩阵分解, 联邦学习, 区块链

Abstract:

Trigger-action programming (TAP) provides a convenient programming paradigm for users to interact with Internet of Things (IoT) devices. Analyzing user-defined TAP rules using machine learning techniques enables functionalities such as TAP rule recommendation and generation, and can improve user experience. However, TAP rules may contain personal privacy information, raising concerns about data upload and sharing. The paper proposes a TAP rule processing system based on federated learning and blockchain technology. Users can train TAP models locally without uploading private data. In order to solve the problem of centralized server single point failure and prevent malicious model parameter uploading, this article uses blockchain technology to improve the centralized TAP federated learning architecture. Users transmit locally updated model gradients to miners in the blockchain for anomaly detection and cross-validation. The mining committee integrated accumulated gradients from normal users to obtain a global model, connected as a block on the blockchain, available for download and use by normal users. The article uses a lightweight unsupervised non-negative matrix factorization method to verify the effectiveness of the proposed distributed learning architecture based on federated learning and blockchain. Experiments confirm that the architecture can effectively protect the privacy in TAP data, and that miners in the blockchain can well identify malicious model parameters, ensuring model stability.

Key words: trigger-action programming, non-negative matrix factorization, federated learning, blockchain

中图分类号:

TP309

薛茗竹, 胡亮, 王明, 王峰. 基于联邦学习和区块链技术的TAP规则处理系统[J]. 信息网络安全, 2024, 24(3): 473-485.

XUE Mingzhu, HU Liang, WANG Ming, WANG Feng. TAP Rule Processing System Based on Federated Learning and Blockchain Technology[J]. Netinfo Security, 2024, 24(3): 473-485.

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	验证用户1	验证用户2	验证用户3	平均Error值
epoch_1_iteration_150	2.73	2.9	2.81	2.81
epoch_2_iteration_75	1.83	1.88	1.86	1.86
epoch_3_iteration_50	1.64	1.64	1.65	1.64
epoch_4_iteration_38	1.58	1.57	1.59	1.58
epoch_10_iteration_15	1.51	1.48	1.50	1.5
epoch_50_iteration_3	1.53	1.51	1.54	1.53
epoch_150_iteration_1	1.51	1.48	1.51	1.5
regular NMF	1.53	1.52	1.52	1.52

	测试用户1	测试用户2	测试用户3	平均Error值
epoch_10_iteration_15	1.57	1.45	1.47	1.49
regular NMF	1.62	1.45	1.54	1.53

随机改变位置的正数数量/个	测试1	测试2	测试3	测试4	测试5	测试6	测试7	测试8	平均值
100	30%	50%	30%	30%	40%	20%	40%	40%	35%
200	60%	60%	50%	80%	50%	70%	60%	60%	61%
300	60%	70%	70%	60%	70%	60%	70%	80%	67%
400	80%	70%	90%	100%	100%	70%	90%	90%	86%
500	90%	90%	90%	90%	90%	90%	90%	100%	91%