mlsanityData to 7 Oct 2026

A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning

arXiv 2209.03885 ↗First posted 8 Sep 2022cs.LG cs.CR cs.DCcited by 10

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Abstract (arXiv)

Federated learning (FL) has emerged as a practical solution to tackle data silo issues without compromising user privacy. One of its variants, vertical federated learning (VFL), has recently gained increasing attention as the VFL matches the enterprises' demands of leveraging more valuable features to build better machine learning models while preserving user privacy. Current works in VFL concentrate on developing a specific protection or attack mechanism for a particular VFL algorithm. In this work, we propose an evaluation framework that formulates the privacy-utility evaluation problem. We then use this framework as a guide to comprehensively evaluate a broad range of protection mechanisms against most of the state-of-the-art privacy attacks for three widely deployed VFL algorithms. These evaluations may help FL practitioners select appropriate protection mechanisms given specific requirements. Our evaluation results demonstrate that: the model inversion and most of the label inference attacks can be thwarted by existing protection mechanisms; the model completion (MC) attack is difficult to be prevented, which calls for more advanced MC-targeted protection mechanisms. Based on our evaluation results, we offer concrete advice on improving the privacy-preserving capability of VFL systems. The code is available at https://github.com/yankang18/Attack-Defense-VFL

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 18 of its 32 references that are in the map. First posted on arXiv 8 Sep 2022.

Builds on

18 of 32 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  3. 2019Deep Leakage from Gradientscited by 1,004
  4. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  5. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  6. 2020iDLG: Improved Deep Leakage from Gradientscited by 367
  7. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 320
  8. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  9. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  10. 2021Label Leakage and Protection in Two-party Split Learningcited by 87
  11. 2019[Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attackscited by 82
  12. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 51
6 more
  1. 2021FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learningcited by 39
  2. 2022Trading Off Privacy, Utility and Efficiency in Federated Learningcited by 33
  3. 2020When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Controlcited by 32
  4. 2021PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakagecited by 28
  5. 2021Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretabilitycited by 28
  6. 2022Residue-based Label Protection Mechanisms in Vertical Logistic Regressioncited by 10

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 5; 2024: 3; 2025: 1.

10 papers.

  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  2. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  3. 2023FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscationcited by 9
  4. 2024FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfercited by 9
  5. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8
  6. 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
  7. 2023Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaborationcited by 6
  8. 2023Secure Vertical Federated Learning Under Unreliable Connectivitycited by 4
  9. 2024FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscationcited by 0
  10. 2025VMask: Tunable Label Privacy Protection for Vertical Federated Learning via Layer Maskingcited by 0