Single-cell RNA sequencing (scRNA-seq) is important to transcriptomic analysis of gene expression. Recently, deep learning has facilitated the analysis of high-dimensional single-cell data. Unfortunately, deep learning models may leak sensitive information about users. As a result, Differential Privacy (DP) is increasingly used to protect privacy. However, existing DP methods usually perturb whole neural networks to achieve differential privacy, and hence result in great performance overheads. To address this challenge, in this paper, we take advantage of the uniqueness of the autoencoder that it outputs only the dimension-reduced vector in the middle of the network, and design a Differentially Private Deep Contrastive Autoencoder Network (DP-DCAN) by partial network perturbation for single-cell clustering. Since only partial network is added with noise, the performance improvement is obvious and twofold: one part of network is trained with less noise due to a bigger privacy budget, and the other part is trained without any noise. Experimental results of six datasets have verified that DP-DCAN is superior to the traditional DP scheme with whole network perturbation. Moreover, DP-DCAN demonstrates strong robustness to adversarial attacks. The code is available at https://github.com/LFD-byte/DP-DCAN.

为了应对单细胞聚类中的隐私保护问题，本文利用自动编码器的独特性，设计了一种差分隐私深度对比自动编码网络(DP-DCAN)，通过部分网络扰动的方式来提升性能并对抗对抗攻击。实验证明，DP-DCAN在六个数据集上的表现优于传统的DP方案，且具有强大的鲁棒性。

DP-DCAN：差分隐私深度对比自编码网络用于单细胞聚类