mlsanityData to 14 Oct 2026

Privacy-Preserving 3-Layer Neural Network Training

arXiv 2308.09531 ↗First posted 18 Aug 2023cs.CR cs.LGcited by 1

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Abstract (arXiv)

In this manuscript, we consider the problem of privacy-preserving training of neural networks in the mere homomorphic encryption setting. We combine several exsiting techniques available, extend some of them, and finally enable the training of 3-layer neural networks for both the regression and classification problems using mere homomorphic encryption technique.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 28 references that are in the map. First posted on arXiv 18 Aug 2023.

Builds on

9 of 28 references.

References · 9

  1. 2018Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inferencecited by 45
  2. 2020POSEIDON: Privacy-Preserving Federated Neural Network Learningcited by 24
  3. 2018Polynomial Regression As an Alternative to Neural Netscited by 15
  4. 2022Privacy-Preserving Logistic Regression Training with A Faster Gradient Variantcited by 7
  5. 2022Volley Revolver: A Novel Matrix-Encoding Method for Privacy-Preserving Neural Networks (Inference)cited by 5
  6. 2022Multinomial Logistic Regression Algorithms via Quadratic Gradientcited by 4
  7. 2022On Polynomial Approximation of Activation Functioncited by 3
  8. 2023Privacy-Preserving CNN Training with Transfer Learning: Multiclass Logistic Regressioncited by 2
  9. 2023Activation Functions Not To Active: A Plausible Theory on Interpreting Neural Networkscited by 2

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024LFFR: Logistic Function For (single-output) Regressioncited by 0