DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clustering
This work addresses privacy concerns for researchers using deep learning in single-cell transcriptomic analysis, though it is incremental as it builds on existing differential privacy techniques.
The authors tackled the problem of privacy leakage in deep learning models for single-cell RNA sequencing by proposing DP-DCAN, a differentially private deep contrastive autoencoder network that uses partial network perturbation, which outperforms traditional whole-network perturbation methods on six datasets.
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.