Cloud-enabled privacy-preserving collaborative learning for mobile sensing
Cloud-enabled privacy-preserving collaborative learning for mobile sensing
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DOI：
10.1145/2426656.2426663
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发表时间：
2012-11
期刊：
影响因子：
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通讯作者：
B. Liu;Yurong Jiang;Fei Sha;R. Govindan
中科院分区：
文献类型：
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作者：
B. Liu;Yurong Jiang;Fei Sha;R. Govindan
在本文中，我们考虑设计一个系统，其中互联网连接的移动的用户贡献传感器数据作为训练样本，并合作建立一个模型，如活动或上下文识别分类任务。构建模型自然可以由云中运行的服务执行，但如果可以确保这些数据的隐私，用户可能更倾向于贡献训练样本。因此，在本文中，我们专注于隐私保护协作学习的移动的设置，它解决了几个竞争的挑战，以前没有考虑在文献中：支持复杂的分类方法，如支持向量机，尊重移动的计算和通信的限制，并使用户确定的隐私级别。我们的方法，泡菜，即使在存在显着扰动的训练样本，确保分类准确性，是强大的方法，试图推断原始数据或中毒的模型，并施加最小的成本。我们使用用户研究，许多现实世界的数据集和两种不同的实现泡菜验证这些说法。
In this paper, we consider the design of a system in which Internet-connected mobile users contribute sensor data as training samples, and collaborate on building a model for classification tasks such as activity or context recognition. Constructing the model can naturally be performed by a service running in the cloud, but users may be more inclined to contribute training samples if the privacy of these data could be ensured. Thus, in this paper, we focus on privacy-preserving collaborative learning for the mobile setting, which addresses several competing challenges not previously considered in the literature: supporting complex classification methods like support vector machines, respecting mobile computing and communication constraints, and enabling user-determined privacy levels. Our approach, Pickle, ensures classification accuracy even in the presence of significantly perturbed training samples, is robust to methods that attempt to infer the original data or poison the model, and imposes minimal costs. We validate these claims using a user study, many real-world datasets and two different implementations of Pickle.