Enabling Secure Intelligent Network with Cloud-Assisted Privacy-Preserving Machine Learning
Enabling Secure Intelligent Network with Cloud-Assisted Privacy-Preserving Machine Learning
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DOI：
10.1109/mnet.2019.1800362
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发表时间：
2019-05
期刊：
影响因子：
9.3
通讯作者：
Yong Yu;Huilin Li;Ruonan Chen;Yanqi Zhao;Haomiao Yang;Xiaojiang Du
中科院分区：
文献类型：
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作者：
Yong Yu;Huilin Li;Ruonan Chen;Yanqi Zhao;Haomiao Yang;Xiaojiang Du
智能网络是指在现有网络中引入认知和协作等智能机制来提高网络性能的网络。安全性在智能网络中是非常重要的，但迄今为止受到的关注较少。在本文中，我们提出了一个框架，该框架在云辅助的隐私保护机器学习的帮助下实现了安全的智能网络。在该框架中，云服务器可以首先使用外包的机器学习算法生成模型，然后使用生成的模型真实的实时处理来自网络的测试数据，这反映到网络上，使其更加智能。同时，该方案保证了训练数据和测试数据的安全性和隐私性，在这个意义上，所提出的框架利用差分隐私来执行隐私保护数据分析和同态加密来对加密数据进行有效操作。该框架中的核心原语，包括差分隐私和同态加密算法的性能评估证明了我们的建议的实用性。
Intelligent networks are regarded as existing networks incorporating some intelligent mechanisms such as cognitive and cooperative approaches to improve network performance. Security is highly essential in intelligent networks but has received less attention so far. In this article, we propose a framework that enables a secure intelligent network with the assistance of cloud-assisted privacy-preserving machine learning. In the framework, the cloud server can first generate a model using outsourced machine learning algorithms and then process testing data from the network with the generated model in real time, which reflects to the network and makes it more intelligent. At the same time, the proposal guarantees the security and privacy of both the training data and the testing data in the sense that the proposed framework takes advantage of differential privacy to perform privacy-preserving data analysis and homomorphic encryption to conduct valid operations over encrypted data. The performance evaluations of the core primitives employed in the framework including differential privacy and homomorphic encryption algorithms demonstrate the practicability of our proposal.