A Survey on Differentially Private Machine Learning [Review Article]

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Mechanism Design via Differential Privacy

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Towards Sharp Inapproximability For Any 2-CSP

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Smooth sensitivity and sampling in private data analysis

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What can we learn privately?

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On the Number of Linear Regions of Deep Neural Networks

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Practical privacy

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Network Embedding as Matrix Factorization

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Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing.

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On k -anonymity and the curse of dimensionality

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Privacy-preserving logistic regression

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Machine Learning Models that Remember Too Much

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Stochastic gradient descent with differentially private updates

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A learning theory approach to non-interactive database privacy

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Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

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Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers

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Data mining with differential privacy

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Statistics on Special Manifolds

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Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing

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Scalable Private Learning with PATE

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PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees

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Functional mechanism

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Differentially Private Generative Adversarial NetworkPreprint

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The cost of privacy

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Computational learning theory

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Hiding the presence of individuals from shared databases

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Differentially Private Distributed Optimization

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Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning

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Differential Privacy and Machine Learning: a Survey and ReviewPreprint

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Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning

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Privacy integrated queries

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Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction

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GANobfuscator: Mitigating Information Leakage Under GAN via Differential Privacy

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Differentially Private Distributed Convex Optimization via Functional Perturbation

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Differentially private Naive Bayes learning over multiple data sources

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A Practical Differentially Private Random Decision Tree Classifier

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Differentially Private Online LearningPreprint

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Differentially Private Mixture of Generative Neural Networks

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Differentially Private K-Means Clustering

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Differentially Private Distributed Online Learning

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Differentially Private Release and Learning of Threshold Functions

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Differentially Private Naive Bayes Classification

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Privacy-preserving generative deep neural networks support clinical data sharingPreprint

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Differentially Private M-Estimators

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Differentially private random decision forests using smooth sensitivity

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Learning Differentially Private Language Models Without Losing Accuracy

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Private, Yet Practical, Multiparty Deep Learning

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Bounds on the sample complexity for private learning and private data release

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On differentially private low rank approximation

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Private Learning and Sanitization: Pure vs. Approximate Differential Privacy

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Sample Complexity Bounds for Differentially Private Learning.

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Differentially Private Learning with Kernels

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Differentially Private Random Forest with High Utility

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Implicit Online Learning

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Differentially Private K-Means Clustering and a Hybrid Approach to Private Optimization

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Differentially Private Releasing via Deep Generative ModelPreprint

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Characterizing the sample complexity of private learners

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Wishart Mechanism for Differentially Private Principal Components Analysis

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Private Collaborative Neural Network Learning.Preprint

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Differentially private subspace clustering

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Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERMPreprint

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Differentially private classification with decision tree ensemble

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On differentially private low rank approximation

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Partitioning-Based Mechanisms Under Personalized Differential Privacy

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DPNE: Differentially Private Network Embedding

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Distributed Differentially Private Algorithms for Matrix and Tensor Factorization

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Sample Complexity Bounds for Differentially Private Learning.

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Differentially Private Compressive K-means

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POSTER

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Embedding differential privacy in decision tree algorithm with different depths

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Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM

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Differentially Private M-Estimators

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Learning Privately with Labeled and Unlabeled Examples

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Near-Optimal Algorithms for Differentially-Private Principal ComponentsPreprint

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Smooth Sensitivity Based Approach for Differentially Private Principal Component AnalysisPreprint

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A survey on federated learning

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A Comprehensive Survey of Privacy-preserving Federated Learning: A Taxonomy, Review, and Future Directions

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Federated Learning for intrusion detection system: Concepts, challenges and future directions

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Synthetic data generation: State of the art in health care domain

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Multiple Strategies Differential Privacy on Sparse Tensor Factorization for Network Traffic Analysis in 5G

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A Survey on Network Slicing Security: Attacks, Challenges, Solutions and Research Directions

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Federated learning: Applications, challenges and future directions

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A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning

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Federated Learning for IoMT Applications: A Standardization and Benchmarking Framework of Intrusion Detection Systems

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Adversarial Attacks Against Deep Generative Models on Data: A Survey

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Trusted AI in Multiagent Systems: An Overview of Privacy and Security for Distributed Learning

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One Parameter Defense—Defending Against Data Inference Attacks via Differential Privacy

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Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review

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How will generative AI disrupt data science in drug discovery?

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A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions

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Artificial intelligence for medicine: Progress, challenges, and perspectives

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A Review on Federated Learning and Machine Learning Approaches: Categorization, Application Areas, and Blockchain Technology

Ogundokun, R. O., Misra, S., Maskeliūnas, R., & Damaševičius, R. (2022) · Information · doi:10.3390/info13050263 · Cited by 74 · see citing works

Differential privacy in deep learning: A literature survey

Pan, K., Ong, Y.-S., Gong, M., Li, H., Qin, A. K., & Gao, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127663 · Cited by 79 · see citing works

Federated Learning on Multimodal Data: A Comprehensive Survey

Lin, Y., Gao, Y., Gong, M., Zhang, S., Zhang, Y., & Li, Z. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1398-0 · Cited by 73 · see citing works

Federated matrix factorization for privacy-preserving recommender systems

Du, Y., Zhou, D., Xie, Y., Shi, J., & Gong, M. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107700 · Cited by 71 · see citing works

Incentive-based demand response optimization method based on federated learning with a focus on user privacy protection

Cheng, H., Lü, T., Hao, R., Li, J., & Ai, Q. (2024) · Applied Energy · doi:10.1016/j.apenergy.2023.122570 · Cited by 71 · see citing works

Differential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive Survey

Husnoo, M. A., Anwar, A., Chakrabortty, R. K., Doss, R., & Ryan, M. J. (2021) · IEEE Access · doi:10.1109/access.2021.3124309 · Cited by 69 · see citing works

Federated FCM: Clustering Under Privacy Requirements

Pedrycz, W. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2021.3105193 · Cited by 67 · see citing works

Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction

Pham, N. D., Abuadbba, A., Gao, Y., Phan, K. T., & Chilamkurti, N. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3274391 · Cited by 61 · see citing works

Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey

Demelius, L., Kern, R., & Trügler, A. (2025) · ACM Computing Surveys · doi:10.1145/3712000 · Cited by 76 · see citing works

Visual privacy attacks and defenses in deep learning: a survey

Zhang, G., Liu, B., Zhu, T., Zhou, A., & Zhou, W. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10123-y · Cited by 53 · see citing works

A survey on privacy inference attacks and defenses in cloud-based Deep Neural Network

Zhang, X., Chen, C., Xie, Y., Chen, X., Zhang, J., & Xiang, Y. (2022) · Computer Standards & Interfaces · doi:10.1016/j.csi.2022.103672 · Cited by 49 · see citing works

Secure collaborative few-shot learning

Xie, Y., Wang, H., Yu, B., & Zhang, C. B. C. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106157 · Cited by 40 · see citing works

Scalable Differential Privacy with Sparse Network Finetuning

Luo, Z., Wu, D. J., Adeli, E., & Fei-Fei, L. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00502 · Cited by 38 · see citing works

PQLM - Multilingual Decentralized Portable Quantum Language Model

Li, S. S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., & Garcia, L. P. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095215 · Cited by 38 · see citing works

Secure and Efficient Federated Learning via Novel Authenticable Multi-Party Computation and Compressed Sensing

Chen, L., Xiao, D., Xiao, X., & Zhang, Y. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3486611 · Cited by 37 · see citing works

Distributed computing in multi-agent systems: a survey of decentralized machine learning approaches

Ahmed, I., Syed, M. A., Maaruf, M., & Khalid, M. (2024) · Computing · doi:10.1007/s00607-024-01356-0 · Cited by 40 · see citing works

Differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems: State-of-the-art and perspectives

Gati, N. J., Yang, L. T., Feng, J., Nie, X., Ren, B., & Tarus, S. K. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.04.017 · Cited by 35 · see citing works

ESB-FL: Efficient and Secure Blockchain-Based Federated Learning With Fair Payment

Chen, B., Zeng, H., Xiang, T., Guo, S., Zhang, T., & Liu, Y. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3177170 · Cited by 28 · see citing works

Differentially Private ADMM Algorithms for Machine Learning

Shang, F., Xu, T., Liu, Y., Liu, H., Shen, L., & Gong, M. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3113768 · Cited by 26 · see citing works

Analysis of Privacy-Enhancing Technologies in Open-Source Federated Learning Frameworks for Driver Activity Recognition

Novikova, E., Fomichov, D., Kholod, I., & Filippov, E. (2022) · Sensors · doi:10.3390/s22082983 · Cited by 24 · see citing works

Development of a Privacy-Preserving UAV System With Deep Learning-Based Face Anonymization

Lee, H., Kim, M. U., Kim, Y., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3113186 · Cited by 23 · see citing works

A systematic review on federated learning system: a new paradigm to machine learning

Chaudhary, R. K., Kumar, R., & Saxena, N. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02257-6 · Cited by 24 · see citing works

Rough Fermatean fuzzy decision-based approach for modelling IDS classifiers in the federated learning of IoMT applications

Albahri, O. S., Al-Samarraay, M. S., Alsattar, H. A., Alamoodi, A. H., Zaidan, A. A., Albahri, A. S., Zaidan, B. B., & Jasim, A. N. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08933-y · Cited by 20 · see citing works

Have You Forgotten? A Method to Assess if Machine Learning Models Have Forgotten Data

Liu, X., & Tsaftaris, S. A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59710-8_10 · Cited by 18 · see citing works

Driver Drowsiness EEG Detection Based on Tree Federated Learning and Interpretable Network

Qin, X., Niu, Y., Zhou, H., Li, X., Jia, W., & Zheng, Y. (2022) · International Journal of Neural Systems · doi:10.1142/s0129065723500090 · Cited by 18 · see citing works

PQSF: post-quantum secure privacy-preserving federated learning

Zhang, X., Deng, H., Wu, R., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-74377-6 · Cited by 18 · see citing works

Privacy and Security in Distributed Learning: A Review of Challenges, Solutions, and Open Research Issues

Afzal, M. U., Abdellatif, A. A., Zubair, M. F., Mehmood, M. Q., & Massoud, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3323932 · Cited by 20 · see citing works

Characterization and Quantification of User Privacy: Key Challenges, Regulations, and Future Directions

Arshad, R., & Asghar, M. R. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3519861 · Cited by 17 · see citing works

Privacy-Preserving Machine Learning Techniques: Cryptographic Approaches, Challenges, and Future Directions

Kucur, E. N., Büyüktanır, T., Ugurelli, M., & Yıldız, K. (2025) · Applied Sciences · doi:10.3390/app16010277 · Cited by 15 · see citing works

Privacy Preservation Using Machine Learning in the Internet of Things

El‐Gendy, S., Elsayed, M. S., Jurcut, A. D., & Azer, M. A. (2023) · Mathematics · doi:10.3390/math11163477 · Cited by 18 · see citing works

Analysis of Machine Learning Models Predicting Quality of Life for Cancer Patients

Savić, M., Kurbalija, V., Ilić, M., Ivanović, M. D., Jakovetić, D., Valachis, A., Autexier, S., Rust, J., & Kosmidis, T. (2021) · International Conference on Management of Emergent Digital EcoSystems (MEDES) · doi:10.1145/3444757.3485103 · Cited by 14 · see citing works

An Adversarial Perspective on Accuracy, Robustness, Fairness, and Privacy: Multilateral-Tradeoffs in Trustworthy ML

Gittens, A., Yener, B., & Yung, M. M. (2022) · IEEE Access · doi:10.1109/access.2022.3218715 · Cited by 15 · see citing works

Radial Basis Function Network with Differential Privacy

Bugshan, N., Khalil, I., Moustafa, N., Almashor, M., & Abuadbba, A. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.09.013 · Cited by 13 · see citing works

GuardianML: Anatomy of Privacy-Preserving Machine Learning Techniques and Frameworks

Njungle, N. B., Jahns, E., Wu, Z., Mastromauro, L., Stojkov, M., & Kinsy, M. A. (2025) · IEEE Access · doi:10.1109/access.2025.3557228 · Cited by 12 · see citing works

Differentially Private Algorithms for Synthetic Power System Datasets

Валентинович, Д. В., & Botterud, A. (2023) · IEEE Control Systems Letters · doi:10.1109/lcsys.2023.3284389 · Cited by 11 · see citing works

Density-based clustering with differential privacy

Wu, F., Du, M., & Zhi, Q. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121211 · Cited by 10 · see citing works

Privacy-Preserving and Interpretable Grade Prediction: A Differential Privacy Integrated TabNet Framework

Zhao, Y., Wang, J., Tan, X., et al. (2025) · Electronics · doi:10.3390/electronics14122328 · Cited by 10 · see citing works

Privacy Auditing in Differential Private Machine Learning: The Current Trends

Namatēvs, I., Sudars, K., Ņikuļins, A., & Ozols, K. (2025) · Applied Sciences · doi:10.3390/app15020647 · Cited by 11 · see citing works

Optimizing Legal Information Access: Federated Search and RAG for Secure AI-Powered Legal Solutions

Amato, F., Cirillo, E., Fonisto, M., et al. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825815 · Cited by 9 · see citing works

FedDAD: Solving the Islanding Problem of SAR Image Aircraft Detection Data

Jia, Z.-W., Zheng, H.-L., Wang, R., & Zhou, W.-G. (2023) · Remote Sensing · doi:10.3390/rs15143620 · Cited by 8 · see citing works

Advances in Differential Privacy and Differentially Private Machine Learning

Das, S., & Mishra, S. (2024) · Springer tracts in electrical and electronics engineering · doi:10.1007/978-981-97-0407-1_7 · Cited by 9 · see citing works

FL-MAC-RDP: Federated Learning over Multiple Access Channels with Rényi Differential Privacy

Wu, S., Yu, M., Ahmed, M. A. M., Qian, Y., & Tao, Y.-H. (2021) · International Journal of Theoretical Physics · doi:10.1007/s10773-021-04867-0 · Cited by 9 · see citing works

Fairness, explainability, privacy, and robustness for trustworthy algorithmic decision-making

Majumdar, S. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85713-0.00017-7 · Cited by 8 · see citing works

Blockchain with Federated Learning for Secure Healthcare Applications

Singh, A., & Singh, K. K. (2023) · doi:10.1002/9781119792406.ch2 · Cited by 8 · see citing works

Differential Privacy: An Umbrella Review

Kilpala, M., Kärkkäinen, T., & Hämäläinen, T. (2022) · Artificial Intelligence and Cybersecurity · doi:10.1007/978-3-031-15030-2_8 · Cited by 7 · see citing works

A differentially private location generalization approach to guarantee non-uniform privacy in moving objects databases

Deldar, F., & Abadi, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107084 · Cited by 7 · see citing works

Self-Sustainable Wearable and Internet of Things (IoT) Devices for Health Monitoring: Opportunities and Challenges

Mercati, P., & Bhat, G. (2024) · IEEE Design and Test · doi:10.1109/mdat.2024.3432862 · Cited by 6 · see citing works

Taming the Triangle: On the Interplays Between Fairness, Interpretability, and Privacy in Machine Learning

Ferry, J., Aïvodji, U., Gambs, S., Huguet, M., & Siala, M. (2025) · Computational Intelligence · doi:10.1111/coin.70113 · Cited by 6 · see citing works

A survey of optimization algorithms for differential privacy in Federated Learning

Shan, F., Liu, Y., Fan, L., Mao, Y., Chen, Z., & Li, S. (2025) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2025.103582 · Cited by 6 · see citing works

Benchmarking Relaxed Differential Privacy in Private Learning: A Comparative Survey

Zheng, Z., Yao, L., Hu, H., & Wu, G. (2025) · ACM Computing Surveys · doi:10.1145/3729216 · Cited by 6 · see citing works

Semi-Private Computation of Data Similarity With Applications to Data Valuation and Pricing

Christensen, R. B., Pandey, S. R., & Popovski, P. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3259879 · Cited by 6 · see citing works

Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?

Perez, I. M., Movahedi, P., Nieminen, V., et al. (2024) · Methods of Information in Medicine · doi:10.1055/a-2385-1355 · Cited by 6 · see citing works

On the local convergence of ADAM-DPGAN with simultaneous and alternating gradient decent training methods

Azadmanesh, M., Ghahfarokhi, B. S., & Talouki, M. A. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125646 · Cited by 5 · see citing works

Roles of Artificial Intelligence--Based Synthetic Data in Health Economics and Outcomes Research

Lai, T. C., & Ngorsuraches, S. (2025) · Value in Health · doi:10.1016/j.jval.2025.04.2157 · Cited by 5 · see citing works

Not a Free Lunch, But a Cheap One: On Classifiers Performance on Anonymized Datasets

Alishahi, M., & Zannone, N. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-81242-3_14 · Cited by 5 · see citing works

Towards Effective Patient Simulators

Liventsev, V., Härmä, A., & Petković, M. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.798659 · Cited by 5 · see citing works

Hybrid Quantum Deep Learning with Differential Privacy for Botnet DGA Detection

Suryotrisongko, H., & Musashi, Y. (2021) · International Conference on Information & Communication Technology and System (ICTS) · doi:10.1109/icts52701.2021.9608217 · Cited by 5 · see citing works

Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations

Jafarigol, E., Trafali̇s, T. B., Razzaghi, T., & Zamankhani, M. (2024) · Springer optimization and its applications · doi:10.1007/978-3-031-74006-0_4 · Cited by 4 · see citing works

Tools and Techniques for Privacy-aware, Edge-centric Distributed Deep Learning

Min, Z., Canady, R. E., Ghosh, U., Gokhale, A. S., & Hakiri, A. (2020) · doi:10.1145/3429882.3430105 · Cited by 4 · see citing works

Integrating Federated Learning in Quantum Computing for Secure and Scalable Model Training

Shareef, D. K., Shakeel, T., Hariprasad, E., et al. (2025) · International Conference on Innovative Mechanisms for Industry Applications · doi:10.1109/icimia67127.2025.11200828 · Cited by 3 · see citing works

VMFL: A Verifiable Multiround Aggregation Scheme for Federated Learning in VANETs

Li, K., Feng, X., Guo, Z., et al. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3593564 · Cited by 3 · see citing works

Data and Model Privacy

Agarwal, S., & Mishra, S. (2021) · Springer eBooks · doi:10.1007/978-3-030-76860-7_8 · Cited by 3 · see citing works

A Study of Differentially Private Machine Learning in Healthcare

Shahid, A. R., & Talukder, S. (2021) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu52992.2021.9598991 · Cited by 3 · see citing works

Evaluating Machine Learning Models for Handwriting Recognition-based Systems under Local Differential Privacy

Shahid, A. R., & Talukder, S. K. (2021) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu52992.2021.9598983 · Cited by 3 · see citing works

Disparity-Aware Federated Learning for Intrusion Detection Systems in Imbalanced Non-IID Settings

Islam, M. M., & Islam, A. B. M. A. A. (2023) · International Conference on Networking, Systems and Security (NSysS) · doi:10.1145/3629188.3629197 · Cited by 2 · see citing works

Differentially-Private Neural Network Training with Private Features and Public Labels

Monir, I. A., & Ghinita, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-68323-7_16 · Cited by 2 · see citing works

FedGSDW:Enhancing Federated Learning Robustness against Model Poisoning Attack

Xiujin, S., Sun, N., Jia-wei, G., & Yu, S. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825224 · Cited by 2 · see citing works

Differentially private tree-based redescription mining

Mihelčić, M., & Miettinen, P. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00934-8 · Cited by 2 · see citing works

Developing and Running Machine Learning Software: Machine Learning Operations (MLOps)

Scutari, M., & Malvestio, M. (2023) · Wiley eBooks · doi:10.1002/9781118445112.stat08455 · Cited by 2 · see citing works

Differentially Private Graph Convolutional Networks with Privacy Amplification

Sun, Y., & Song, M. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00321 · Cited by 1 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Evaluating 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 · see citing works

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 · see citing works

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 · see citing works

Collaborative 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 · see citing works

Annotation-Guided Code Partitioning with Homomorphic Encryption for Privacy Preserving Cloud ComputingPreprint

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 · see citing works

De-identifying transmission system using wireless channel as differential privacy noise and deep neural networks

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

Towards the design of personal data protection-aware artificial intelligence applications in ubiquitous smart environments

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

ФОРМИРОВAНИЕ ГОТОВНОСТИ БУДУЩИХ ПЕДAГОГОВ К ОРГAНИЗAЦИИ РAБОТЫ ПО РAЗВИТИЮ ВAЛЕОЛОГИЧЕСКОЙ КУЛЬТУРЫ ШКОЛЬНИКОВ

БАЗАРБАЕВА, Ә. А., Майгельдиева, Ш. М., & ЖҮНДІБАЕВА, Т. Н. (2023) · Pedagogy and Psychology · doi:10.51889/2077-6861.2023.1.30.016

चितलवाना पंचायत समिति में मानव गरीबी सूचकांक - 2016 ( à¤à¤• गà¥à¤°à¤¾à¤® सà¥à¤¤à¤°à¥€à¤¯ à¤à¥Œà¤—ोलिक अधà¥à¤¯à¤¯à¤¨ )

Gaurav, S. K., & Swarnkar, P. (2017) · International research journal of multidisciplinary studies

FEATURES AND DIFFERENCES OF ADEQUATE AND EQUIVALENT TRANSLATION

Кенжебaевa, У., Nadirova, G. E., & Seitmetova, Z. (2018) · DOAJ (DOAJ: Directory of Open Access Journals)

Коммуникaтивно- прaгмaтический aнaлиз дипломaтических бумaг (нa основе вербaльных нот)

Кaмирдиновa, Н., & Кaлиевa, Ш. С. (2018) · DOAJ (DOAJ: Directory of Open Access Journals)

Aдміністрaтивно-прaвове регулювaння інвестиційної діяльності в Укрaїні

Пaлaмaрчук, І. В. (2014)