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Distributed Vector-OLE

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A privacy-preserving and non-interactive federated learning scheme for regression training with gradient descent

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A survey: Distributed Machine Learning for 5G and beyond

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When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control

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Scalable Privacy-Preserving Distributed Learning

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Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data

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Practical and Robust Federated Learning With Highly Scalable Regression Training

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PILOT: Practical Privacy-Preserving Indoor Localization Using OuTsourcing

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An Efficient Learning Framework for Federated XGBoost Using Secret Sharing and Distributed Optimization

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Large-scale Secure XGB for Vertical Federated Learning

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Privacy-preserving dataset combination and Lasso regression for healthcare predictions

van Egmond, M. B., Spini, G., van der Galiën, O., IJpma, A. S., Veugen, T., Kraaij, W., Sangers, A., Rooijakkers, T., Langenkamp, P. N., Kamphorst, B., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01582-y · Cited by 46 · 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 Computation for Machine Learning With SPDZPreprint

Chen, V., Pastro, V., & Raykova, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.00329 · Cited by 42 · see citing works

Secure Logistic Regression for Vertical Federated Learning

He, D., Du, R., Zhu, S., Zhang, M., Liang, K., & Chan, S. (2021) · IEEE Internet Computing · doi:10.1109/mic.2021.3138853 · Cited by 40 · see citing works

Collaborative Machine Learning: Schemes, Robustness, and Privacy

Wang, J., Pal, A., Yang, Q., Kant, K., Zhu, K., & Guo, S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3169347 · Cited by 42 · see citing works

Vertical federated learning: a structured literature review

Khan, A., ten Thij, M., & Wilbik, A. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02356-y · Cited by 40 · see citing works

A Survey and Guideline on Privacy Enhancing Technologies for Collaborative Machine Learning

Soykan, E. Ü., Karaçay, L., Karakoç, F., & Tomur, E. (2022) · IEEE Access · doi:10.1109/access.2022.3204037 · Cited by 42 · see citing works

Privacy-Preserving Linear Regression on Distributed Data by Homomorphic Encryption and Data Masking

Qiu, G., Gui, X., & Zhao, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3000764 · Cited by 39 · see citing works

A critical overview of privacy-preserving approaches for collaborative forecasting

Gonçalves, C., Bessa, R. J., & Pinson, P. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2020.06.003 · Cited by 39 · see citing works

Cryptϵ

Chowdhury, A. R., Wang, C., He, X., Machanavajjhala, A., & Jha, S. (2020) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3318464.3380596 · Cited by 38 · see citing works

QUOTIENT: Two-Party Secure Neural Network Training and PredictionPreprint

Agrawal, N., Shamsabadi, A. S., Kusner, M. J., & Gascón, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.03372 · Cited by 38 · see citing works

PrivColl: Practical Privacy-Preserving Collaborative Machine Learning

Zhang, Y., Bai, G., Li, X., Curtis, C., Chen, C., & Ko, R. K. L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58951-6_20 · Cited by 37 · see citing works

Blind Justice: Fairness with Encrypted Sensitive AttributesPreprint

Kilbertus, N., Gascón, A., Kusner, M. J., Veale, M., Gummadi, K. P., & Weller, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.03281 · Cited by 30 · see citing works

Achieve Efficient and Privacy-Preserving Disease Risk Assessment Over Multi-Outsourced Vertical Datasets

Wang, F., Zhu, H., Lu, R., Zheng, Y., & Li, H. (2020) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2020.3026631 · Cited by 27 · see citing works

AgrEvader: Poisoning Membership Inference against Byzantine-robust Federated Learning

Zhang, Y., Bai, G., Chamikara, M. A. P., Ma, M., Shen, L., Wang, J., Nepal‬, ‪., Xue, M., Wang, L., & Liu, J. K. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583542 · Cited by 27 · see citing works

Oblivious coopetitive analytics using hardware enclaves

Dave, A., Leung, C., Popa, R. A., Gonzalez, J. E., & Stoica, I. G. (2020) · European Conference on Computer Systems · doi:10.1145/3342195.3387552 · Cited by 27 · see citing works

Privacy-Preserving Graph Neural Network for Node Classification.Preprint

Zhou, J., Chen, C., Zheng, L., Zheng, X., Wu, B., Liu, Z., & Wang, L. (2020) · arXiv (Cornell University) · Cited by 27 · see citing works

A Graph Federated Architecture with Privacy Preserving Learning

Rizk, E., & Sayed, A. H. (2021) · IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc51858.2021.9593148 · Cited by 26 · see citing works

More Practical Privacy-Preserving Machine Learning as A Service via Efficient Secure Matrix Multiplication

Lu, W.-J., & Sakuma, J. (2018) · doi:10.1145/3267973.3267976 · Cited by 25 · see citing works

Generative Table Pre-training Empowers Models for Tabular Prediction

Zhang, T., Wang, S., Yan, S., Li, J., & Liu, Q. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.917 · Cited by 30 · see citing works

Dynamic Games for Social Model Training Service Market via Federated Learning Approach

Cheng, W., Zou, Y., Xu, J., & Liu, W. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3086100 · Cited by 22 · see citing works

PSI from Ring-OLE

Chongchitmate, W., Ishai, Y., Lu, S., & Ostrovsky, R. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3559378 · Cited by 23 · see citing works

PrivEdge: From Local to Distributed Private Training and Prediction

Shamsabadi, A. S., Gascón, A., Haddadi, H., & Cavallaro, A. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2988132 · Cited by 22 · see citing works

Securing private information by data perturbation using statistical transformation with three dimensional shearing

Kumar, G. S., & Premalatha, K. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107819 · Cited by 21 · see citing works

Towards federated learning: An overview of methods and applications

Silva, P. R., Vinagre, J., & da Gama, J. M. P. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1486 · Cited by 22 · see citing works

Privacy-preserving two-parties logistic regression on vertically partitioned data using asynchronous gradient sharing

Wei, Q., Li, Q., Zhou, Z., Ge, Z., & Zhang, Y. (2020) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-020-01017-x · Cited by 20 · see citing works

Enforcing Privacy in Distributed Learning With Performance Guarantees

Rizk, E., Vlaski, S., & Sayed, A. H. (2023) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3316590 · Cited by 20 · see citing works

High-Precision Privacy-Preserving Real-Valued Function Evaluation

Boura, C., Chillotti, I., Gama, N., Jetchev, D., Peceny, S., & Petric, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-662-58387-6_10 · Cited by 19 · see citing works

Assisted Learning: A Framework for Multi-Organization LearningPreprint

Xian, X., Wang, X., Ding, J., & Ghanadan, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00566 · Cited by 19 · see citing works

Vertical Federated Learning Across Heterogeneous Regions for Industry 4.0

Zhang, R. Y., Li, H., Tian, L., Hao, M., & Zhang, Y. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3393492 · Cited by 17 · see citing works

Concurrent vertical and horizontal federated learning with fuzzy cognitive maps

Salmeron, J. L., & Arévalo, I. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107482 · Cited by 17 · see citing works

A Hybrid-Domain Framework for Secure Gradient Tree Boosting.Preprint

Fang, W., Chen, C., Tan, J., Yu, C., Lu, Y., Wang, L., Wang, L., Zhou, J., & -, X. A. (2020) · arXiv (Cornell University) · Cited by 17 · see citing works

Inference attacks based on GAN in federated learning

Ha, T., & Dang, T. K. (2022) · International Journal of Web Information Systems · doi:10.1108/ijwis-04-2022-0078 · Cited by 16 · see citing works

Blind Federated Learning without initial model

Salmeron, J. L., & Arévalo, I. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00911-y · Cited by 15 · see citing works

Harmonic Coding: An Optimal Linear Code for Privacy-Preserving Gradient-Type Computation

Yu, Q., & Avestimehr, A. S. (2019) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2019.8849245 · 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

A Novel Secure Vector Product for Protecting the Privacy of Data in Vertically Partitioned Dataset

Sharma, V., Bhushan, S., Singh, A. K., & Kumar, P. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-2828-4_28 · Cited by 14 · see citing works

Orchard: Differentially Private Analytics at Scale

Roth, E., Zhang, H., Haeberlen, A., & Pierce, B. C. (2020) · Operating Systems Design and Implementation · Cited by 14 · see citing works

DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural NetworksPreprint

Hashemi, H., Wang, Y., & Annavaram, M. (2020) · arXiv.org · doi:10.48550/arxiv.2006.01300 · Cited by 27 · see citing works

Federated Learning: Concepts, Challenges and Implementation

Khan, N., Nisar, S., Khan, M. A., Khan, M. A., Camacho, D., Rehman, Y. A. U., & Hussain, A. (2025) · Expert Systems · doi:10.1111/exsy.70096 · Cited by 13 · see citing works

Privacy-Preserving Multivariant Regression Analysis over Blockchain-Based Encrypted IoMT Data

Haque, R. U., & Hasan, A. S. M. T. (2021) · Studies in big data · doi:10.1007/978-3-030-74575-2_3 · Cited by 13 · see citing works

CostCO: An automatic cost modeling framework for secure multi-party computation

Fang, V., Brown, L., Lin, W., Zheng, W., Panda, A., & Popa, R. A. (2022) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp53844.2022.00017 · Cited by 13 · see citing works

SCALR: Communication-Efficient Secure Multi-Party Logistic Regression

Lu, X., Sami, H. U., & Güler, B. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2023.3308954 · Cited by 13 · see citing works

Privacy Preserving Recursive Least Squares Solutions

Tjell, K., Cascudo, I., & Wisniewski, R. (2019) · European Control Conference (ECC) · doi:10.23919/ecc.2019.8796169 · Cited by 13 · see citing works

DRL-KeyAgree: An Intelligent Combinatorial Deep Reinforcement Learning-Based Vehicular Platooning Secret Key Generation

Kurunathan, H., Li, K., Tovar, E., Jorge, A., Ni, W., & Jamalipour, A. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3410534 · Cited by 12 · see citing works

Privacy-Preserving Federated Data Analysis: Data Sharing, Protection, and Bioethics in Healthcare

Choudhury, A., Sun, C., Dekker, A. L. A. J., Dumontier, M., & van Soest, J. (2022) · Springer eBooks · doi:10.1007/978-3-030-83047-2_8 · Cited by 12 · see citing works

Efficient Asynchronous Multi-Participant Vertical Federated Learning

Shi, H., Xu, Y., Jiang, Y., Yu, H., & Cui, L. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3201729 · Cited by 12 · see citing works

Efficient optimisation framework for convolutional neural networks with secure multiparty computation

Berry, C., & Komninos, N. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102679 · Cited by 12 · see citing works

Efficient Privacy-Preserving Stochastic Nonconvex OptimizationPreprint

Wang, L., Jayaraman, B., Evans, D., & Gu, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.13659 · Cited by 12 · see citing works

Privacy-Preserving XGBoost InferencePreprint

Meng, X., & Joan Feigenbaum, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.04789 · Cited by 14 · see citing works

An Intelligent Approach for Protecting Privacy in Distributed Information Mining Using Secured Computation of Multiple Participating Sites

Sharma, V., Bhushan, S., Boahar, B. S., Kumar, P., & Kumar, A. (2021) · Ingénierie des systèmes d information · doi:10.18280/isi.260601 · Cited by 11 · see citing works

Communication-Efficient Secure Logistic Regression

Agarwal, A., Peceny, S., Raykova, M., Schoppmann, P., & Seth, K. (2024) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp60621.2024.00031 · Cited by 11 · see citing works

Convex optimization-based Privacy-Preserving Distributed Least Squares via Subspace Perturbation

Li, Q., Heusdens, R. R., & Christensen, M. G. (2020) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco47968.2020.9287473 · Cited by 10 · see citing works

Privacy-Preserving Federated Recurrent Neural Networks

Sav, S., Diaa, A., Pyrgelis, A., Bossuat, J.-P., & Hubaux, J. (2023) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2023-0122 · Cited by 10 · see citing works

Secure Multiparty Matrix Multiplication Based on Strassen-Winograd Algorithm

Dumas, J., Lafourcade, P., Fenner, J. L., Lucas, D., Orfila, J.-B., Pernet, C., & Puys, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-26834-3_5 · Cited by 9 · see citing works

Vertical Federated Learning for Higher-Order Factorization Machines

Atarashi, K., & Ishihata, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-75765-6_28 · Cited by 8 · see citing works

Privacy-enhanced and non-interactive linear regression with dropout-resilience

He, G., Ren, Y., Bian, M., Feng, G., & Zhang, X. (2023) · Information Sciences · doi:10.1016/j.ins.2023.02.080 · Cited by 8 · see citing works

When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk ControlPreprint

Chen, C., Zhou, J., Wang, L., Wu, X., Fang, W., Tan, J., Wang, L., Liu, A. X., Wang, H., & Hong, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.08753 · Cited by 8 · see citing works

Secure and Scalable Document Similarity on Distributed Databases: Differential Privacy to the Rescue

Schoppmann, P., Vogelsang, L., Gascón, A., & Balle, B. (2020) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2020-0024 · Cited by 7 · see citing works

Privacy Preserving Feature Selection for Sparse Linear Regression

Akavia, A., Galili, B., Shaul, H., Weiss, M., & Zohar Yakhini, B. (2023) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2024-0017 · Cited by 7 · see citing works

Privacy-preserving Linear Regression Scheme and Its Application

You, W. W.-Y. L. (2022) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.11896/jsjkx.220300190

Fast, Privacy Preserving Linear Regression over Distributed Datasets based on Pre-Distributed Data

Cock, M. D., Dowsley, R., Nascimento, A. C. A., & Newman, S. (2015) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/2808769.2808774 · Cited by 86 · see citing works

Privacy Preservation Linear Regression Scheme with ApplicationsPreprint

Shen, E. Z. (2022) · Research Square · doi:10.21203/rs.3.rs-2165500/v1

Privacy-Preserving Linear Regression on Distributed Data by Homomorphic Encryption and Data Masking

Qiu, G., Gui, X., & Zhao, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3000764 · Cited by 39 · see citing works

Privacy Preserving Linear Regression on Distributed Databases

Dankar, F. K. (2015) · Transactions on data privacy · doi:10.5555/2870564.2870566 · Cited by 20 · see citing works