mlsanityData to 7 Oct 2026

PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learning

arXiv 2312.10380 ↗First posted 16 Dec 2023cs.LGcited by 4

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Abstract (arXiv)

Federated learning (FL) has attracted growing attention since it allows for privacy-preserving collaborative training on decentralized clients without explicitly uploading sensitive data to the central server. However, recent works have revealed that it still has the risk of exposing private data to adversaries. In this paper, we conduct reconstruction attacks and enhance inference attacks on various datasets to better understand that sharing trained classification model parameters to a central server is the main problem of privacy leakage in FL. To tackle this problem, a privacy-preserving image distribution sharing scheme with GAN (PPIDSG) is proposed, which consists of a block scrambling-based encryption algorithm, an image distribution sharing method, and local classification training. Specifically, our method can capture the distribution of a target image domain which is transformed by the block encryption algorithm, and upload generator parameters to avoid classifier sharing with negligible influence on model performance. Furthermore, we apply a feature extractor to motivate model utility and train it separately from the classifier. The extensive experimental results and security analyses demonstrate the superiority of our proposed scheme compared to other state-of-the-art defense methods. The code is available at https://github.com/ytingma/PPIDSG.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 38 references that are in the map. First posted on arXiv 16 Dec 2023.

Builds on

23 of 38 references.

References · 23

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  4. 2019Deep Leakage from Gradientscited by 1,004
  5. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  6. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  7. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 475
  8. 2020iDLG: Improved Deep Leakage from Gradientscited by 367
  9. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
  10. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 320
  11. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  12. 2023Are Diffusion Models Vulnerable to Membership Inference Attacks?cited by 135
11 more
  1. 2020Provable Defense against Privacy Leakage in Federated Learning from Representation Perspectivecited by 94
  2. 2020InstaHide: Instance-hiding Schemes for Private Distributed Learningcited by 90
  3. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  4. 2021HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Imagescited by 62
  5. 2020DaST: Data-free Substitute Training for Adversarial Attackscited by 60
  6. 2021Towards General Deep Leakage in Federated Learningcited by 40
  7. 2018Encryption-then-Compression Systems using Grayscale-based Image Encryption for JPEG Imagescited by 33
  8. 2020Privacy-preserving Collaborative Learning with Automatic Transformation Searchcited by 31
  9. 2022Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defensecited by 16
  10. 2020DeepEDN: A Deep Learning-based Image Encryption and Decryption Network for Internet of Medical Thingscited by 11
  11. 2023Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learningcited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Fast John Ellipsoid Computation with Differential Privacy Optimizationcited by 7
  2. 2024Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgerycited by 2
  3. 2025Secure Generalization through Stochastic Bidirectional Parameter Updates Using Dual-Gradient Mechanismcited by 0
  4. 2026FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generationcited by 0