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A Survey on Differentially Private Machine Learning [Review Article]
Abstract
Recent years have witnessed remarkable successes of machine learning in various applications. However, machine learning models suffer from a potential risk of leaking private information contained in training data, which have attracted increasing research attention. As one of the mainstream privacy- preserving techniques, differential privacy provides a promising way to prevent the leaking of individual-level privacy in training data while preserving the quality of training data for model building. This work provides a comprehensive survey on the existing works that incorporate differential privacy with machine learning, so- called differentially private machine learning and categorizes them into two broad categories as per different differential privacy mechanisms: the Laplace/ Gaussian/exponential mechanism and the output/objective perturbation mechanism. In the former, a calibrated amount of noise is added to the non-private model and in the latter, the output or the objective function is perturbed by random noise. Particularly, the survey covers the techniques of differentially private deep learning to alleviate the recent concerns about the privacy of big data contributors. In addition, the research challenges in terms of model utility, privacy level and applications are discussed. To tackle these challenges, several potential future research directions for differentially private machine learning are pointed out.
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“Differentially private (DP) deep learning (using a privatized gradient descent optimizer [1]) has been adopted to protect users and avoid legal risks of sensitive data use [27, 29].”
From Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settings (2024) · cites this paper as [29]
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FedPDM: Representation enhanced federated learning with privacy preserving diffusion models
Guo, W., Zhuang, F., Tong, Y., Zhang, X., Hu, Z., Zhao, J., & Dong, J. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115452 · Cited by 1
Noise-Efficient Learning of Differentially Private Partitioning Machine Ensembles
Huang, Z., Lei, Y., & Kabán, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26412-2_36 · Cited by 1
Data analysis with performance and privacy enhanced classification
Tajanpure, R., & Muddana, A. L. (2023) · Journal of Intelligent Systems · doi:10.1515/jisys-2022-0215 · Cited by 1
“He presented an approach balancing privacy and the utility of data [21].”
BackgroundEvaluating Cluster-Based Synthetic Data Generation for Blood-Transfusion Analysis
Kroes, S. K. S., van Leeuwen, M., Groenwold, R. H. H., & Janssen, M. P. (2023) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp3040040 · Cited by 1
Shielding sensitive medical imaging data
Liu, G., Wang, C., & Xia, T. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00865-z · Cited by 1
An Interpretable Graph-Based Mapping of Trustworthy Machine Learning Research
Derzsy, N., Majumdar, S., & Malik, R. (2021) · Springer proceedings in complexity · doi:10.1007/978-3-030-81854-8_7 · Cited by 1
“A number of papers in Table 3 are on topics that have received less attention in TwML literature [9,16], such as reinforcement learning, active learning, bandit algorithms, and outlier detection.”
BackgroundCollaborative Private Classifiers Construction
Alishahi, M., & Moghtadaiee, V. (2023) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-031-16088-2_2 · Cited by 1
Kim, D., Lee, Y., Cheon, S., Choi, H., Lee, J., Youm, H., Lee, D., & Kim, H. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4636541 · Cited by 1
Lee, H., Ahn, H., & Park, Y. D. (2022) · ICT Express · doi:10.1016/j.icte.2022.09.002
Machine Learning-Based Data Analytics With Privacy
Tajanpure, R., & Muddana, A. L. (2023) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-6684-6519-6.ch005
Morales-Vanegas, E. A., Magallán, B. A. Á., Gaytan-Lugo, L. S., & Santana-Mancilla, P. C. (2023) · Avances en Interacción Humano-Computadora · doi:10.47756/aihc.y8i1.133
Optimal Obfuscation to Protect Client Privacy in Federated Learning
Guan, B., Wei, S., Houmansadr, A., et al. (2024) · Conference on Information Sciences and Systems · doi:10.1109/ciss59072.2024.10480208
Density-Based Clustering with Differential PrivacyPreprint
Wu, F., Du, M., & Zhi, Q. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4798792
A Review of Privacy-Preserving Decentralized Learning
Gitanshu, & Kumar, N. B. M. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-2839-8_29
Metadata
- Authors
- Gong, M., Xie, Y., Pan, K., Feng, K., & Qin, A. K.
- Affiliations
- Xidian University (CN)
- Swinburne University of Technology (AU)
- Venue
- IEEE Computational Intelligence Magazine
- Published
- 2020-05
- Volume
- 15
- Issue
- 2
- Pages
- 49-64
- ISSN
- 1556-603X (ISSN-L)
- 1556-6048
- OpenAlex
- W3016000170
- Semantic Scholar
- 0e9c4e5761d97bf538692c11cd267ad7affe708f
- Cited by
- 115
- References
- 107
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 28 · 2023: 27 · 2022: 15 · 2021: 23 · 2020: 4
- FWCI
- 6.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Computational Intelligence Magazine (publishedVersion)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Funders
- National Basic Research Program of China (2017YFB0802200)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Keywords
- differential privacy, privacy-preserving machine learning, Laplace mechanism, Gaussian mechanism, exponential mechanism, output perturbation, data privacy, model utility, privacy-utility trade-off
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