DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for
  Single-cell Clustering
Huifa Li Jie Fu Zhili Chen Xiaomin Yang Haitao Liu Xinpeng Ling
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.