基于独立随机投影的物联网轻量级隐私保护协同学习研究

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Linshan Jiang, Rui Tan, Xin Lou, Guosheng Lin
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引用次数: 8

摘要

物联网(IoT)将成为实现更好的系统智能的主要数据生成基础设施。本文考虑了一种实用的保护隐私的协作学习方案的设计和实现,其中好奇的学习协调器根据许多物联网对象提供的数据样本训练更好的机器学习模型,同时保护训练数据原始形式的机密性不受协调器的影响。现有的分布式机器学习和数据加密方法会产生大量的计算和通信开销,使得它们不适合资源受限的物联网对象。我们研究了一种方法,该方法在每个物联网对象上应用独立随机投影来混淆数据,并基于来自物联网对象的投影数据在协调器上训练深度神经网络。这种方法为物联网对象引入了少量的计算开销,并将大部分工作负载转移给具有足够计算资源的协调器。尽管物联网对象执行的独立预测解决了好奇的协调器和一些受损物联网对象之间的潜在勾结,但它们显着增加了预测数据的复杂性。在本文中,我们利用深度学习的优越学习能力来捕获复杂的模式,以保持良好的学习性能。广泛的比较评估表明,这种方法优于其他轻量级方法,这些方法在具有轻度到中度数据模式复杂性的应用程序中对差分隐私和/或支持向量机应用加性噪声进行学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Lightweight Privacy-preserving Collaborative Learning for Internet of Things by Independent Random Projections
The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This article considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator. Existing distributed machine learning and data encryption approaches incur significant computation and communication overhead, rendering them ill-suited for resource-constrained IoT objects. We study an approach that applies independent random projection at each IoT object to obfuscate data and trains a deep neural network at the coordinator based on the projected data from the IoT objects. This approach introduces light computation overhead to the IoT objects and moves most workload to the coordinator that can have sufficient computing resources. Although the independent projections performed by the IoT objects address the potential collusion between the curious coordinator and some compromised IoT objects, they significantly increase the complexity of the projected data. In this article, we leverage the superior learning capability of deep learning in capturing sophisticated patterns to maintain good learning performance. Extensive comparative evaluation shows that this approach outperforms other lightweight approaches that apply additive noisification for differential privacy and/or support vector machines for learning in the applications with light to moderate data pattern complexities.
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来源期刊
CiteScore
5.20
自引率
3.70%
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