CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning

This repository contains code for the paper "CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning". We implement an attack to measure the empirical privacy of federated training pipelines based on designing out-of-distribution examples which we call canaries.

Our implementation is based on a modified FLSim but the code contained within canife can be used in any (PyTorch-based) FL framework of your choice.

Installation

Via pip and anaconda

conda create -n "canife" python=3.9 
conda activate canife
pip install -r ./requirements.txt

Paper Replication

CelebA

To replicate the above plot on CelebA with epsilon=50 first clone the LEAF repo and then under ../leaf/data/celeba/ run

./preprocess.sh -s niid --sf 1.0 -k 0 -t user

This should form splits all_data_0_0_keep_0_train_9.json and all_data_0_0_keep_0_test_9.json under ../leaf/data/celeba/train/ and ..leaf/data/celeba/test/ respectively.

To begin training with periodic attacking run the command:

python launcher.py --dataset celeba --dump-path DUMP_PATH --data-root DATA_ROOT --epsilon 50 --canary-setup holdout --canary-test-type train_freeze --canary-loss loss2 --canary-design-pool-size 100 --canary-design-minibatch-size num_users --canary-num-test-batches 100 --canary-insert-offset 40 --users-per-round 100 --local-batch-size 128 --canary-epochs 2000 --fl-epochs 30 --model-arch resnet --canary-norm-matching True --canary-norm-constant 5 --device cpu

Where:

• DATA_ROOT is the path to the CelebA LEAF data folder i.e .../leaf/data/celeba
• DUMP_PATH is the path for CANIFE to output experiment training plots and logs which by default is ./local_checkpoints

One can also set --plot-path PLOT_PATH to output to DUMP_PATH/PLOT_PATH/ (by default plot_path=""). Change --device cpu to --device gpu to run on a single GPU.

NOTE: ./local_checkpoints/ has a toy example attack output (from a checkpointed DP model on Sent140) which should be deleted before running any new experiments. Alternatively, change the --plot-path arg to a new folder within ./local_checkpoints/.

Once the experiment has been completed you can extract the data and plot as follows:

• Extract attack logs from attack checkpoints (.tar files) by running the script python extract_exp.py --path DUMP_PATH/ --csv-name celeba_eps50_tf.csv
• Plot the attack using the script python plot_sweep.py --csv-path PATH_TO_CSV/celeba_eps50_tf.csv

This will output the figure under /plotting/

NOTE: To generate the full figure, rerun the experiment with --epsilon equal to 10 and 30 with different --plot-path args, extract each .csv and combine them before plotting.

Other Datasets

For other LEAF datasets make the following changes:

• Ensure you have the correct splits under leaf/data see the LEAF repo for installation instructions

  • For Shakespeare:
    • Use LEAF preprocess cmd: ./preprocess.sh -s niid --sf 1.0 -k 0 -t sample -tf 0.8 in ..leaf/data/shakespeare/
    • Will form splits all_data_0_0_keep_0_train_9.json and all_data_0_0_keep_0_test_9.json
  • For Sent140:
    • Use LEAF preprocess cmd: ./preprocess.sh -s niid --sf 1.0 -k 0 -t user in ../leaf/data/sent140/
    • Will form splits all_data_0_15_keep_1_train_6.json and all_data_0_15_keep_1_test_6.json

• For running Shakespeare:

  • Add/replace args --model-arch shakes-lstm --dataset shakespeare --users-per-round 60 --fl-epochs 30 --fl-client-lr 3 --local-batch-size 128 --canary-insert-offset 8 --canary-design-pool-size 100

• For running Sent140:

  • Add/replace args --model-arch lstm --dataset sent140 --users-per-round 100 --fl-epochs 15 --local-batch-size 32 --canary-insert-offset 100 --canary-design-pool-size 800

Reference

If the code and/or paper contained in this repository were useful to you please consider citing this work:

@article{maddock2022canife,
  title={{CANIFE}: Crafting Canaries for Empirical Privacy Measurement in Federated Learning},
  author={Maddock, Samuel and Sablayrolles, Alexandre and Stock, Pierre},
  journal={arXiv preprint arXiv:2210.02912},
  year={2022}
}

Contributing

See the CONTRIBUTING for how to contribute to this library.

License

This code is released under BSD-3-Clause, as found in the LICENSE file.