Analyzing the Leaky Cauldron

The goal of this project is to evaluate the privacy leakage of differential private machine learning algorithms.

The code has been adapted from the code base of membership inference attack work by Shokri et al.

Below we describe the setup and installation instructions. To run the experiments for the following projects, refer to their respective README files (hyperlinked):

• Evaluating Differentially Private Machine Learning in Practice (evaluating_dpml\)
• Revisiting Membership Inference Under Realistic Assumptions (improved_mi\)

Software Requirements

• Python 3.8
• Tensorflow : To use Tensorflow with GPU, cuda-toolkit-11 and cudnn-8 are also required.
• Tensorflow Privacy

Installation Instructions

Assuming the system has Ubuntu 18.04 OS. The easiest way to get Python 3.8 is to install Anaconda 3 followed by installing the dependencies via pip. The following bash code installs the dependencies (including scikit_learn, tensorflow>=2.4.0 and tf-privacy) in a virtual environment:

$ python3 -m venv env
$ source env/bin/activate
$ python3 -m pip install --upgrade pip
$ python3 -m pip install --no-cache-dir -r requirements.txt

Furthermore, to use cuda-compatible nvidia gpus, the following script should be executed (copied from Tensorflow website) to install cuda-toolkit-11 and cudnn-8 as required by tensorflow-gpu:

# Add NVIDIA package repositories
$ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
$ sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
$ sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
$ sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"
$ sudo apt-get update

$ wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb

$ sudo apt install ./nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
$ sudo apt-get update

$ wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
$ sudo apt install ./libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
$ sudo apt-get update

# Install development and runtime libraries (~4GB)
$ sudo apt-get install --no-install-recommends \
    cuda-11-0 \
    libcudnn8=8.0.4.30-1+cuda11.0  \
    libcudnn8-dev=8.0.4.30-1+cuda11.0

# Reboot. Check that GPUs are visible using the command: nvidia-smi

# Install TensorRT. Requires that libcudnn8 is installed above.
$ sudo apt-get install -y --no-install-recommends libnvinfer7=7.1.3-1+cuda11.0 \
    libnvinfer-dev=7.1.3-1+cuda11.0 \
    libnvinfer-plugin7=7.1.3-1+cuda11.0

Pre-processing data sets

Pre-processed CIFAR-100 data set has been provided in the dataset/ folder. Purchase-100 data set can be downloaded from Kaggle web site. This can be pre-processed using the preprocess_purchase.py script provided in the extra/ folder. Alternatively, the files for Purchase-100 data set can be found here. For pre-processing other data sets, bound the L2 norm of each record to 1 and pickle the features and labels separately into $DATASET_feature.p and $DATASET_labels.p files in the dataset/ folder (where $DATASET is a placeholder for the data set file name, e.g. for Purchase-100 data set, $DATASET will be purchase_100).