mlsanityData to 7 Oct 2026

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

arXiv 2008.08753 ↗First posted 20 Aug 2020cs.CR cs.LGcited by 32

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Abstract (arXiv)

Logistic Regression (LR) is the most widely used machine learning model in industry for its efficiency, robustness, and interpretability. Due to the problem of data isolation and the requirement of high model performance, many applications in industry call for building a secure and efficient LR model for multiple parties. Most existing work uses either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they incur potential security risks. SS based methods have provable security, but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security. We then present the distributed implementation of CAESAR for scalability requirement. We have deployed CAESAR in a risk control task and conducted comprehensive experiments. Our experimental results show that CAESAR improves the state-of-the-art model by around 130 times.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Aug 2020.

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Citing papers per year: 2021: 2; 2022: 9; 2023: 6; 2024: 6; 2025: 6; 2026: 3.

32 papers.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  2. 2023FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Modelscited by 62
  3. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  4. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  5. 2023Reducing Communication for Split Learning by Randomized Top-k Sparsificationcited by 22
  6. 2022Exploiting Data Sparsity in Secure Cross-Platform Social Recommendationcited by 17
  7. 2022A Hybrid Self-Supervised Learning Framework for Vertical Federated Learningcited by 17
  8. 2023A Survey for Federated Learning Evaluations: Goals and Measurescited by 16
  9. 2022A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learningcited by 10
  10. 2023BadVFL: Backdoor Attacks in Vertical Federated Learningcited by 9
  11. 2022Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyondcited by 7
  12. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
20 more
  1. 2021Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutationcited by 5
  2. 2022EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Partycited by 5
  3. 2024Privacy-Preserving Diffusion Model Using Homomorphic Encryptioncited by 4
  4. 2025Vertical Federated Learning in Practice: The Good, the Bad, and the Uglycited by 4
  5. 2022UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworkscited by 3
  6. 2025Tree-based Models for Vertical Federated Learning: A Surveycited by 3
  7. 2025From Risk to Resilience: Towards Assessing and Mitigating the Risk of Data Reconstruction Attacks in Federated Learningcited by 3
  8. 2021SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Treecited by 2
  9. 2025Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNscited by 2
  10. 2024Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural networkcited by 1
  11. 2024PackVFL: Efficient HE Packing for Vertical Federated Learningcited by 1
  12. 2022Scalable and Sparsity-Aware Privacy-Preserving K-means Clustering with Application to Fraud Detectioncited by 0
  13. 2022HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Predictioncited by 0
  14. 2024Active-Passive Federated Learning for Vertically Partitioned Multi-view Datacited by 0
  15. 2024Stalactite: Toolbox for Fast Prototyping of Vertical Federated Learning Systemscited by 0
  16. 2025VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learningcited by 0
  17. 2025Secure Sparse Matrix Multiplications and their Applications to Privacy-Preserving Machine Learningcited by 0
  18. 2026Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulationcited by 0
  19. 2026GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNscited by 0
  20. 2026A Bayesian Vertical Federated Learning Framework for Multivariate Reduced-Rank High-Dimensional Regressioncited by 0