The emerging applications of machine learning algorithms on mobile devices motivate us to offload the computation tasks of training a model or deploying a trained one to the cloud or at the edge of the network. One of the major challenges in this setup is to guarantee the privacy of the client data. Various methods have been proposed to protect privacy in the literature. Those include (i) adding noise to the client data, which reduces the accuracy of the result, (ii) using secure multiparty computation (MPC), which requires significant communication among the computing nodes or with the client, (iii) relying on homomorphic encryption (HE) methods, which significantly increases computation load at the servers. In this paper, we propose $\textit{Corella}$ as an alternative approach to protect the privacy of data. The proposed scheme relies on a cluster of servers, where at most $T \in \mathbb{N}$ of them may collude, each running a learning model (e.g., a deep neural network). Each server is fed with the client data, added with $\textit{strong}$ noise, independent from user data. The variance of the noise is set to be large enough to make the information leakage to any subset of up to $T$ servers information-theoretically negligible. On the other hand, the added noises for different servers are $\textit{correlated}$. This correlation among the queries allows the parameters of the models running on different servers to be $\textit{trained}$ such that the client can mitigate the contribution of the noises by combining the outputs of the servers, and recover the final result with high accuracy and with a minor computational effort. Simulation results for various datasets demonstrate the accuracy of the proposed approach for the classification, using deep neural networks, and the autoencoder, as supervised and unsupervised learning tasks, respectively.

中文翻译：

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Corella：一种基于相关查询的私有多服务器学习方法

机器学习算法在移动设备上的新兴应用促使我们将训练模型或将训练好的模型部署到云或网络边缘的计算任务卸载。此设置中的主要挑战之一是保证客户端数据的隐私。已经提出了各种方法来保护文献中的隐私。这些包括 (i) 向客户端数据添加噪声，这会降低结果的准确性，(ii) 使用安全多方计算 (MPC)，这需要计算节点之间或与客户端之间的大量通信，(iii) 依赖同态加密 (HE) 方法，这显着增加了服务器的计算负载。在本文中，我们提出 $\textit{Corella}$ 作为保护数据隐私的替代方法。所提出的方案依赖于一组服务器，其中最多 $T \in \mathbb{N}$ 可能串通，每个都运行一个学习模型（例如，深度神经网络）。每个服务器都收到客户端数据，加上 $\textit{strong}$ 噪声，独立于用户数据。噪声的方差设置得足够大，以使得信息泄漏到最多 $T$ 服务器信息的任何子集 - 理论上可以忽略不计。另一方面，不同服务器增加的噪音是 $\textit{correlated}$。查询之间的这种相关性允许在不同服务器上运行的模型的参数为 $\textit{trained}$，这样客户端可以通过组合服务器的输出来减轻噪声的贡献，并以高准确性和少量的计算工作。