Code for the linear- and tree-model experiments in the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"
Note: The Netflix Prize dataset is not included in this repository due to its size
The code was (lightly) tested and should be compatible with both Python 2.7 and Python 3.6 on Bash.
To reproduce the results in the paper, clone the repository and go to the code/ directory.
Then, run ./run-errors.sh to compute the training and test errors.
This is required before running any of the other experiments.
The script should take about 1-2 hours to run.
Run ./run-membership.sh.
The results will be found in results-sklearn/iwpc/membership/ and results-sklearn/eyedata/membership/.
Run ./run-attribute.sh cyp2c9 and ./run-attribute.sh vkorc1.
The results will be found in results-sklearn/iwpc/attribute/.
Run ./run-reduction.sh cyp2c9 and ./run-reduction.sh vkorc1.
The results will be found in results-sklearn/iwpc/reduction/.