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Defending SplitNN with Noise

Applying noise to Split Neural networks. Code for Titcombe, T., Hall, A. J., Papadopoulos, P., & Romanini, D. (2021). Practical Defences Against Model Inversion Attacks for Split Neural Networks. arXiv preprint arXiv:2104.05743. (link)

Summary

Motivation

Data input SplitNNs have been shown to be susceptible to, amongst other attacks, black box model inversion. In this attack, an adversary trains an "inversion" model to turn intermediate data (data sent between model parts) back into raw input data. This attack is a particularly relevant for a computational server colluding with a data holder. Applying differential privacy directly to the model (differentially private stochastic gradient descent - the Abadi method) does not defend against this attack as output from a trained model part is deterministic and therefore a decoder model can be trained.

Aims

This project aims to protect SplitNNs from black box model inversion attack by adding noise to the data being transferred between model parts. The idea is that the stochasticity of intermediate data can stop a model from learning to invert it back into raw data. Additionally, we combine the noise addition with NoPeekNN, in which the model learns to create an intermediate distribution as uncorrelated with the input data as possible. While NoPeekNN does not provide any guarantees on data leakage, unlike differential privacy, we aim to demonstrate that it can provide some protection against a model inversion attack.

Get started

Requirements

Developed in Python 3.8, but similar minor versions should work.

Environment

A conda environment, dpsnn, has been provided with all packages required to run the experiments, including the local source code (Pytorch-cpu only - remove cpuonly to enable GPU computation).

  • Run conda env create -f environment.yml to create the environment using the latest packages OR
  • Run conda env create -f environment-lock.yml to use fixed package versions (for reproducibility).
  • conda activate dpsnn to activate the environment

Build from source

To install the local source code only:

  1. Clone this repo
  2. In a terminal, navigate to the repo
  3. Run pip install -e ..

This installs the local package dpsnn.

Train models

Scripts to train a classifier and attacker can be found in scripts:

  • python scripts/train_model.py --noise_scale <noise_level> --nopeek_weight <weight> to train a differentially private model using noise drawn from Laplacian distribution with scale <noise_level> and NoPeek loss weighted by <weight>.

  • python scripts/train_attacker.py --model <name> to train an attacker on a trained model, <name>

Classifiers are stored in models/classifiers and are named like mnist_<noise>noise_<nopeek>nopeek_epoch=<X>.ckpt, where <noise> is the scale of laplacian noise added to the intermediate tensor during training as a decimal. ...05noise means scale 0.5, ...10noise means scale 1.0. <nopeek> is the weighting of NoPeek loss in the loss function, using the same decimal scheme as with noise. <X> is the number of training epochs during which the classifier was performing the best.

Attack models are stored in models/attackers and are named like mnist_attacker_model<<classifier>>_set<noise>noise.ckpt, where <classifier> is the stem (everything but the ckpt suffix) of the classifier it's attacking. <noise> refers to the scale of noise applied to the intermediate tensor after training. _set<noise>noise is not included if the noise scale of the classifier does not change from what it was trained on.

Run experiments

To replicate all experiments present in the paper, run ./main.sh <arg>, where <arg> is:

  • noise to train models with noise
  • nopeek to train models with NoPeek
  • combo to train models with both NoPeek and noise
  • plain to train a model without defences
  • performance to calculate the accuracy and Distance Correlation of each model in models/classifiers/
  • all to run all experiments

Notebooks

We have provided relevant analysis in the notebooks/ folder. Be aware that previous exploratory notebooks were removed. Look over previous commits for a full history of experimentation.

Data

The data/ folder is intentionally left empty to preserve the project structure. This project uses the MNIST and EMNIST datasets. Each dataset will be downloaded to data/ when first used by a script.

Contributing

If you have a question about the paper/ experiments/ results, or have noticed a bug in the code, please open an issue in this repository.

If you are providing code, please follow these conventions:

  • black to format code
  • isort to format imports
  • Add type hints
  • Add docstrings to functions and classes
  • Use pytorch_lightning to build PyTorch models

Publications

Titcombe, T., Hall, A. J., Papadopoulos, P., & Romanini, D. (2021). Practical Defences Against Model Inversion Attacks for Split Neural Networks. arXiv preprint arXiv:2104.05743. (link)

You can cite this work using:

@article{titcombe2021practical,
    title={Practical Defences Against Model Inversion Attacks for Split Neural Networks},
    author={Titcombe, Tom and Hall, Adam J and Papadopoulos, Pavlos and Romanini, Daniele},
    journal={arXiv preprint arXiv:2104.05743},
    year={2021}
}

License

Apache 2.0. See the full license.