# DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clustering

arXiv: https://arxiv.org/abs/2311.03410 · HTML: https://mlsanity.com/paper/2311.03410/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 78 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 19 of 19)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [Does Learning Require Memorization? A Short Tale about a Long Tail](https://mlsanity.com/paper/1906.05271/) (2019)
- [Differential Privacy Has Disparate Impact on Model Accuracy](https://mlsanity.com/paper/1905.12101/) (2019)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Scalable Private Learning with PATE](https://mlsanity.com/paper/1802.08908/) (2018)
- [Hypothesis Testing Interpretations and Renyi Differential Privacy](https://mlsanity.com/paper/1905.09982/) (2019)
- [One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy](https://mlsanity.com/paper/2203.06580/) (2022)
- [Differentially Private Learning with Per-Sample Adaptive Clipping](https://mlsanity.com/paper/2212.00328/) (2022)
- [Gaussian Membership Inference Privacy](https://mlsanity.com/paper/2306.07273/) (2023)
- [Bias and Variance of Post-processing in Differential Privacy](https://mlsanity.com/paper/2010.04327/) (2020)
- [Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise](https://mlsanity.com/paper/2211.15893/) (2022)
- [Towards Understanding the Impact of Model Size on Differential Private Classification](https://mlsanity.com/paper/2111.13895/) (2021)
- [Towards Effective Differential Privacy Communication for Users' Data Sharing Decision and Comprehension](https://mlsanity.com/paper/2003.13922/) (2020)
- [Genomic Data Sharing under Dependent Local Differential Privacy](https://mlsanity.com/paper/2102.07357/) (2021)
- [De-Health: All Your Online Health Information Are Belong to Us](https://mlsanity.com/paper/1902.00717/) (2019)

## Built on by (most cited first, 1 of 1)

- [Single-cell Curriculum Learning-based Deep Graph Embedding Clustering](https://mlsanity.com/paper/2408.10511/) (2024)

## Abstract (arXiv)

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.