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Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19
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The paper discusses how Differential Privacy (specifically DPSGD from [1]) impacts model performance for underrepresented groups.


Configure environment by running: pip install -r requirements.txt

We use Python3.7 and GPU Nvidia TitanX.

File allows run the code. It uses utils/params.yaml to set parameters from the paper and builds a graph on Tensorboard. For Sentiment prediction we use


  1. MNIST (part of PyTorch)
  2. Diversity in Faces (obtained from IBM here)
  3. iNaturalist (download from here)
  4. UTKFace (from here)
  5. AAE Twitter corpus (from here)

We use copied from public repo

DP-FedAvg implementation is taken from public repo

Implementation of DPSGD is based on TF Privacy repo and papers:

[1] M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang. Deep learning with differential privacy. In CCS, 2016.

[2] H. B. McMahan and G. Andrew. A general approach to adding differential privacy to iterative training procedures. arXiv:1812.06210, 2018

[3] H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang. Learning differentially private recurrent language models. In ICLR, 2018

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