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Feature Inference Attack on Model Predictions in Vertical Federated Learning

arXiv 2010.10152 ↗First posted 20 Oct 2020cs.LG cs.DBcited by 106

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Abstract (arXiv)

Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data collaboration without revealing their private data to each other. Recently, vertical FL, where the participating organizations hold the same set of samples but with disjoint features and only one organization owns the labels, has received increased attention. This paper presents several feature inference attack methods to investigate the potential privacy leakages in the model prediction stage of vertical FL. The attack methods consider the most stringent setting that the adversary controls only the trained vertical FL model and the model predictions, relying on no background information. We first propose two specific attacks on the logistic regression (LR) and decision tree (DT) models, according to individual prediction output. We further design a general attack method based on multiple prediction outputs accumulated by the adversary to handle complex models, such as neural networks (NN) and random forest (RF) models. Experimental evaluations demonstrate the effectiveness of the proposed attacks and highlight the need for designing private mechanisms to protect the prediction outputs in vertical FL.

Key facts. Cited by 106 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Oct 2020.

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Citing papers per year: 2021: 11; 2022: 25; 2023: 24; 2024: 28; 2025: 16; 2026: 2.

106 papers.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  2. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 84
  3. 2021Defending against Model Stealing via Verifying Embedded External Featurescited by 46
  4. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
  5. 2021FedIPR: Ownership Verification for Federated Deep Neural Network Modelscited by 36
  6. 2023LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulationcited by 36
  7. 2022Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guaranteescited by 30
  8. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  9. 2022FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learningcited by 23
  10. 2021User-Level Label Leakage from Gradients in Federated Learningcited by 21
  11. 2023FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMscited by 21
  12. 2021Defending against Reconstruction Attack in Vertical Federated Learningcited by 20
38 more
  1. 2024LightTR: A Lightweight Framework for Federated Trajectory Recoverycited by 19
  2. 2021Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputscited by 18
  3. 2021Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learningcited by 17
  4. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  5. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  6. 2024OpenFGL: A Comprehensive Benchmark for Federated Graph Learningcited by 16
  7. 2022Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clusteringcited by 14
  8. 2024Deep Efficient Private Neighbor Generation for Subgraph Federated Learningcited by 14
  9. 2024Trustworthy Distributed AI Systems: Robustness, Privacy, and Governancecited by 14
  10. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  11. 2024GI-NAS: Boosting Gradient Inversion Attacks Through Adaptive Neural Architecture Searchcited by 14
  12. 2022Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated Learningcited by 13
  13. 2022Federated Learning Attacks and Defenses: A Surveycited by 12
  14. 2023BAFFLE: A Baseline of Backpropagation-Free Federated Learningcited by 12
  15. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  16. 2023Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learningcited by 11
  17. 2021A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learningcited by 10
  18. 2022Residue-based Label Protection Mechanisms in Vertical Logistic Regressioncited by 10
  19. 2022HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learningcited by 10
  20. 2023FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscationcited by 10
  21. 2022ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systemscited by 9
  22. 2022Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithmcited by 9
  23. 2023Passive Inference Attacks on Split Learning via Adversarial Regularizationcited by 9
  24. 2024Feature Inference Attack on Shapley Valuescited by 9
  25. 2024Federated Graph Learning for Cross-Domain Recommendationcited by 9
  26. 2025Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attackscited by 9
  27. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8
  28. 2023Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attackscited by 8
  29. 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
  30. 2024Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Datacited by 8
  31. 2022Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyondcited by 7
  32. 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Partiescited by 7
  33. 2023High Dimensional Distributed Gradient Descent with Arbitrary Number of Byzantine Attackerscited by 7
  34. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  35. 2022Secure Forward Aggregation for Vertical Federated Neural Networkscited by 6
  36. 2024Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspectivecited by 6
  37. 2024A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasurescited by 6
  38. 2022MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clusteringcited by 5

50 most cited of 106 All 106 (JSON)