mlsanityData to 14 Oct 2026

Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond

arXiv 2207.09087 ↗First posted 19 Jul 2022cs.CR cs.LGcited by 7

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Abstract (arXiv)

We consider vertical logistic regression (VLR) trained with mini-batch gradient descent -- a setting which has attracted growing interest among industries and proven to be useful in a wide range of applications including finance and medical research. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks, where the protocols might differ between one another, yet a procedure of obtaining local gradients is implicitly shared. We first consider the honest-but-curious threat model, in which the detailed implementation of protocol is neglected and only the shared procedure is assumed, which we abstract as an oracle. We find that even under this general setting, single-dimension feature and label can still be recovered from the other party under suitable constraints of batch size, thus demonstrating the potential vulnerability of all frameworks following the same philosophy. Then we look into a popular instantiation of the protocol based on Homomorphic Encryption (HE). We propose an active attack that significantly weaken the constraints on batch size in the previous analysis via generating and compressing auxiliary ciphertext. To address the privacy leakage within the HE-based protocol, we develop a simple-yet-effective countermeasure based on Differential Privacy (DP), and provide both utility and privacy guarantees for the updated algorithm. Finally, we empirically verify the effectiveness of our attack and defense on benchmark datasets. Altogether, our findings suggest that all vertical federated learning frameworks that solely depend on HE might contain severe privacy risks, and DP, which has already demonstrated its power in horizontal federated learning, can also play a crucial role in the vertical setting, especially when coupled with HE or secure multi-party computation (MPC) techniques.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 41 of its 80 references that are in the map. First posted on arXiv 19 Jul 2022.

Builds on

41 of 80 references.

References · 41

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29 more
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 1.

7 papers.

  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  2. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  3. 2023FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscationcited by 10
  4. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  5. 2023Online Vertical Federated Learning for Cooperative Spectrum Sensingcited by 6
  6. 2023Mutual Information Regularization for Vertical Federated Learningcited by 4
  7. 2025Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigmscited by 0