Can We Use Split Learning on 1D CNN for Privacy Preserving Training?

Note: this repo contains our implementation for our ACM ASIACCS 2020 paper below. Please if you find it useful, use the below citation to cite our paper.

Sharif Abuadbba, Kyuyeon Kim, Minki Kim, Chandra Thapa, Seyit A. Camtepe, Yansong Gao, Hyoungshick Kim, Surya Nepal, ‘Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?’, The 15th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS 2020), Taipei, Taiwan, from June 1st to June 5th, 2020

Available Now:

• Our 1D CNN split learning models with their accuracy results.
• Our pre-processed training/testing samples of MIT arrhythmia ECG database.
• Our privacy leakage 3 measurements results using visual invertibility; distance correlation; and Dynamic Time Warping.
• Our proposed two countermeasures results: i) increasing the number of layers in a CNN model and ii) using differential privacy.

Repository summary

• csv directory: results in csv format from various kinds of experiments.
  • adding_layers directory: experiment results of adding more convolutional layer on 1D CNN.
    • accuracy directory: has best test accuracy data retrieved from each run with different number of convolutional layers.
    • trainlog directory: has train loss, train accuracy, test loss, test accuracy data for each epoch while training 1D CNN having different number of convolutional layers.
  • diffpriv directory: experiment results of applying differential privacy on split layer in 1D CNN.
    • accuracy directory: has best test accuracy data retrieved from applying different strength of differential privacy.
    • trainlog directory: has train loss, train accuracy, test loss, test accuracy data for each epoch while training 1D CNN whose split layer is differential private.
  • measurement directory:
    • dcor directory: has distribution and mean of distance correlation data from each split layer filter.
    • dtw directory: has distribution and mean of DTW data from each split layer filter.
  • split_nonsplit directory: has train log data from split and non-split 1D CNN which are used to prove that they have same results.
• figure directory: source codes in ipynb format which give figure with data in csv directory.
• measurement directory: source codes in ipynb format which measure distance correlation and DTW between raw data and data from split layer filters.
  • adding_layers directory: measure distance correlation with different number of convolutional layers.
  • diffpriv directory: measure DTW with different strength of differential privacy on split layer.
• mitdb directory: has preprocessed train and test data in hdf5 format.