Adaptive Machine Unlearning

This repository contains the implementation of the experiments shown in Adaptive Machine Unlearning.

Requirements

Experiments were run using Python 3.8.10. To install dependencies:

pip install -r requirements.txt

In addition to installing the required dependencies, you will need to install JAX. For example, to install for CPU:

pip install --upgrade pip
pip install --upgrade jax jaxlib

To install for GPU, the command you run will be dependent on your device. For example, if you're running CUDA 11.0 (as used for our experiments):

pip install --upgrade pip
pip install --upgrade jax jaxlib==0.1.67+cuda110 -f https://storage.googleapis.com/jax-releases/jax_releases.html

Evaluation and Results for Figure 1

To read the precomputed results:

cd white-box
python read_results.py

This should output the following results, and save the plots for Figure 1 under white-box/plots:

cifar (6)

indicator	acc (after)	acc (bef)	noise	shard pred acc.	hash
[0.890, 0.952]	0.507 ± 0.025	0.572 ± 0.011	0	0.303 ± 0.005	2001210bc030deaf83697e3ef79c205b
[0.784, 0.869]	0.504 ± 0.020	0.554 ± 0.010	0.15	0.257 ± 0.004	c308616d0d4d32be976679494164ba06
[0.670, 0.772]	0.487 ± 0.021	0.525 ± 0.011	0.22	0.229 ± 0.003	c64ffa14ecfecab4e7ce8c403b4e438f
[0.490, 0.603]	0.455 ± 0.025	0.484 ± 0.012	0.3	0.205 ± 0.003	2d8bc173c2bbc3ff07640a56378c3d10

cifar (2)

indicator	acc (after)	acc (bef)	noise	shard pred acc.	hash
[0.947, 0.987]	0.448 ± 0.033	0.521 ± 0.019	0	0.655 ± 0.012	cca5d44d6ef698eaaebd26797fefd6d4
[0.797, 0.881]	0.433 ± 0.026	0.475 ± 0.015	0.2	0.587 ± 0.007	9727ef9007c04b81d4fdd8dc31523fbc
[0.638, 0.744]	0.419 ± 0.025	0.452 ± 0.015	0.25	0.567 ± 0.006	fd6e44d21d63050ee933d283291bb72d
[0.493, 0.607]	0.399 ± 0.027	0.427 ± 0.015	0.3	0.550 ± 0.005	e5b23c4e74a3d1e36e877f0b7dc75aea

fmnist (6)

indicator	acc (after)	acc (bef)	noise	shard pred acc.	hash
[0.819, 0.899]	0.849 ± 0.011	0.874 ± 0.004	0	0.248 ± 0.007	88366775d1d313ed6523390b93f2eb64
[0.662, 0.765]	0.838 ± 0.011	0.854 ± 0.005	0.4	0.215 ± 0.005	646f56308738e45cd7c92163c655ee30
[0.540, 0.652]	0.823 ± 0.009	0.834 ± 0.006	0.6	0.198 ± 0.004	e5152be9a1bb7d5d9b7e0cd4f0491ed3
[0.477, 0.590]	0.810 ± 0.012	0.820 ± 0.006	0.75	0.190 ± 0.004	74471a75cbe765e236d1e118330557c4

fmnist (2)

indicator	acc (after)	acc (bef)	noise	shard pred acc.	hash
[0.976, 0.999]	0.826 ± 0.016	0.863 ± 0.006	0	0.597 ± 0.014	5a38e3f38e604447be3f771336bacb00
[0.797, 0.881]	0.808 ± 0.013	0.828 ± 0.007	0.5	0.555 ± 0.010	262b4889a7c914d3090aeab8b0fd0bf4
[0.607, 0.715]	0.791 ± 0.016	0.807 ± 0.008	0.7	0.538 ± 0.007	25186293067603719e741adc8f7572d5
[0.497, 0.610]	0.763 ± 0.020	0.781 ± 0.009	1	0.523 ± 0.005	4acc53823282eda5ef3c416e598a7326

mnist (6)

indicator	acc (after)	acc (bef)	noise	shard pred acc.	hash
[0.849, 0.922]	0.973 ± 0.004	0.978 ± 0.002	0	0.201 ± 0.004	ad013be6f8ffd0c0451cbfbb35f00ab2
[0.729, 0.824]	0.965 ± 0.005	0.969 ± 0.003	0.4	0.186 ± 0.003	3ccca21040cf98e7195bc7e8e9d6808b
[0.583, 0.694]	0.940 ± 0.009	0.945 ± 0.004	0.8	0.178 ± 0.003	da2b1393772670f14febb4d53bb892ad
[0.493, 0.607]	0.913 ± 0.018	0.923 ± 0.007	1.1	0.176 ± 0.003	88b6ecd38063816e135611ce9bf52237

mnist (2)

indicator	acc (after)	acc (bef)	noise	shard pred acc.	hash
[0.927, 0.976]	0.962 ± 0.007	0.971 ± 0.003	0	0.540 ± 0.006	a6e06d50a5fd48e6e6e32602b6565406
[0.769, 0.857]	0.959 ± 0.008	0.968 ± 0.003	0.8	0.534 ± 0.006	243ddb045d377a4f42d03ae9aed48e9a
[0.587, 0.697]	0.953 ± 0.007	0.962 ± 0.004	1.3	0.530 ± 0.006	d4b3eb93d07c8e2e014f59e48e1eac5d
[0.497, 0.610]	0.949 ± 0.008	0.957 ± 0.004	1.6	0.527 ± 0.006	6b71a0616160fb739d89c703001548ff

Training for Figure 1

The following commands were run to generate the models for Figure 1:

python experiment.py --experiment_path configs/cifar_6/0_0.json
python experiment.py --experiment_path configs/cifar_6/0_15.json
python experiment.py --experiment_path configs/cifar_6/0_22.json
python experiment.py --experiment_path configs/cifar_6/0_3.json

python experiment.py --experiment_path configs/cifar_2/0_0.json
python experiment.py --experiment_path configs/cifar_2/0_2.json
python experiment.py --experiment_path configs/cifar_2/0_25.json
python experiment.py --experiment_path configs/cifar_2/0_3.json

python experiment.py --experiment_path configs/fmnist_6/0_0.json
python experiment.py --experiment_path configs/fmnist_6/0_4.json
python experiment.py --experiment_path configs/fmnist_6/0_6.json
python experiment.py --experiment_path configs/fmnist_6/0_75.json

python experiment.py --experiment_path configs/fmnist_2/0_0.json
python experiment.py --experiment_path configs/fmnist_2/0_5.json
python experiment.py --experiment_path configs/fmnist_2/0_7.json
python experiment.py --experiment_path configs/fmnist_2/1_0.json

python experiment.py --experiment_path configs/mnist_6/0_0.json
python experiment.py --experiment_path configs/mnist_6/0_4.json
python experiment.py --experiment_path configs/mnist_6/0_8.json
python experiment.py --experiment_path configs/mnist_6/1_1.json

python experiment.py --experiment_path configs/mnist_2/0_0.json
python experiment.py --experiment_path configs/mnist_2/0_8.json
python experiment.py --experiment_path configs/mnist_2/1_3.json
python experiment.py --experiment_path configs/mnist_2/1_6.json

To retrain from scratch, remove all of the result directories under white-box/results and run these commands. If you run as-is, it will only add new trials to the existing results, not overwrite them.

Training for Appendix C.2

Simply execute the following and the given script will run and print the results cited:

cd black-box
python main.py