Abstract
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 being 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. Firstly, we use contrastive learning to enhance the feature extraction of the autoencoder. And then, 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 8 datasets have verified that DP-DCAN is superior to the traditional DP scheme with whole network perturbation. The code is available at https://github.com/LFD-byte/DP-DCAN.
H. Li and J. Fu—Contributing equally to this work.
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References
Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308–318 (2016)
Chen, J., Wang, W.H., Shi, X.: Differential privacy protection against membership inference attack on machine learning for genomic data. In: BIOCOMPUTING 2021: Proceedings of the Pacific Symposium, pp. 26–37. World Scientific (2020)
Chen, X., He, K.: Exploring simple Siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750–15758 (2021)
Eraslan, G., Simon, L.M., Mircea, M., Mueller, N.S., Theis, F.J.: Single-cell RNA-seq denoising using a deep count autoencoder. Nat. Commun. 10(1), 390 (2019)
Flores, M., et al.: Deep learning tackles single-cell analysis—a survey of deep learning for scRNA-seq analysis. Brief. Bioinform. 23(1), bbab531 (2022)
Fu, J., et al.: Differentially private federated learning: a systematic review. arXiv preprint arXiv:2405.08299 (2024)
Fu, J., et al.: DPSUR: accelerating differentially private stochastic gradient descent using selective update and release. arXiv preprint arXiv:2311.14056 (2023)
Ha, T., Dang, T.K., Dang, T.T., Truong, T.A., Nguyen, M.T.: Differential privacy in deep learning: an overview. In: 2019 International Conference on Advanced Computing and Applications (ACOMP), pp. 97–102. IEEE (2019)
Leemann, T., Pawelczyk, M., Kasneci, G.: Gaussian membership inference privacy. In: Advances in Neural Information Processing Systems, vol. 36 (2024)
Liu, H., Wu, Z., Peng, C., Lei, X., Tian, F., Lu, L.: Adaptive differential privacy of character and its application for genome data sharing. In: 2019 International Conference on Networking and Network Applications (NaNA), pp. 429–436. IEEE (2019)
Oestreich, M., Chen, D., Schultze, J.L., Fritz, M., Becker, M.: Privacy considerations for sharing genomics data. EXCLI J. 20, 1243 (2021)
Shen, Y., Wang, Z., Sun, R., Shen, X.: Towards understanding the impact of model size on differential private classification. arXiv preprint arXiv:2111.13895 (2021)
Wang, J., Xia, J., Wang, H., Su, Y., Zheng, C.H.: scDCCA: deep contrastive clustering for single-cell RNA-Seq data based on auto-encoder network. Brief. Bioinform. 24(1), bbac625 (2023)
Wang, J., et al.: ScGNN is a novel graph neural network framework for single-cell RNA-Seq analyses. Nat. Commun. 12(1), 1882 (2021)
Wu, X., Wei, Y., Mao, Y., Wang, L.: A differential privacy DNA motif finding method based on closed frequent patterns. Clust. Comput. 22(Suppl. 2), 2907–2919 (2019)
Xia, T., et al.: Differentially private learning with per-sample adaptive clipping. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 10444–10452 (2023)
Yan, J., Ma, M., Yu, Z.: bmVAE: a variational autoencoder method for clustering single-cell mutation data. Bioinformatics 39(1), btac790 (2023)
Yilmaz, E., Ji, T., Ayday, E., Li, P.: Genomic data sharing under dependent local differential privacy. In: Proceedings of the Twelfth ACM Conference on Data and Application Security and Privacy, pp. 77–88 (2022)
Acknowledgments
This work is supported by the Natural Science Foundation of Shanghai (Grant No. 22ZR1419100), the National Natural Science Foundation of China Key Program (Grant No. 62132005), and CAAI-Huawei MindSpore Open Fund (Grant No. CAAIXSJLJJ-2022-005A).
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Li, H., Fu, J., Chen, Z., Yang, X., Liu, H., Ling, X. (2024). DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-Cell Clustering. In: Huang, DS., Zhang, Q., Guo, J. (eds) Advanced Intelligent Computing in Bioinformatics. ICIC 2024. Lecture Notes in Computer Science(), vol 14881. Springer, Singapore. https://doi.org/10.1007/978-981-97-5689-6_33
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DOI: https://doi.org/10.1007/978-981-97-5689-6_33
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