Privacy-preserving protocols for perceptron learning algorithm in neural networks

Saeed Samet, Ali Miri
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引用次数: 15

Abstract

Neural networks have become increasingly important in areas such as medical diagnosis, bio-informatics, intrusion detection, and homeland security. In most of these applications, one major issue is preserving privacy of individualpsilas private information and sensitive data. In this paper, we propose two secure protocols for perceptron learning algorithm when input data is horizontally and vertically partitioned among the parties. These protocols can be applied in both linearly separable and non-separable datasets, while not only data belonging to each party remains private, but the final learning model is also securely shared among those parties. Parties then can jointly and securely apply the constructed model to predict the output corresponding to their target data. Also, these protocols can be used incrementally, i.e. they process new coming data, adjusting the previously constructed network.
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神经网络感知器学习算法的隐私保护协议
神经网络在医学诊断、生物信息学、入侵检测和国土安全等领域变得越来越重要。在大多数这些应用程序中,一个主要问题是保护个人隐私信息和敏感数据的隐私。在本文中,我们提出了两种感知器学习算法的安全协议,当输入数据在各方之间被水平和垂直分割时。这些协议可以应用于线性可分和不可分的数据集,而不仅属于每一方的数据保持私有,而且最终的学习模型也在这些各方之间安全地共享。然后,各方可以联合并安全地应用构建的模型来预测与目标数据对应的输出。此外,这些协议可以增量地使用,即它们处理新的到来的数据,调整先前构建的网络。
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