Awesome

auditing-dpsgd

Code for Auditing Differentially Private Machine Learning: How Private is Private SGD?. There is another implementation in the TensorFlow Privacy repo which is simpler to run than this implementation but harder to parallelize.

To reproduce results, first modify code/auditing_args.py to set the appropriate variables, based on the examples from code/args/. For example, getting the ClipBKD plot from Figure 1a uses the following setting:

args = {

        "pois_ct": [1, 2, 4, 8],

        "clip_norm": [1],

        "init_mult": [1],

        "trials": (0, 1000),

        "dataset": "fmnist",

        "old_bkd": False,

        "model": "2f",

        "save_dir": "../../test/fmnist_2f",

        "data_dir": "../datasets"

}

"pois_ct": list of poisoning counts, a subset of [1, 2, 4, 8]

"clip_norm": list of clipping norms, can take any float

"init_mult": list of initialization randomness scales, can take any float

"trials": (trial_start_index, trial_end_index)

"dataset": a string representing the dataset

"old_bkd":  True if using old backdoor, False else

"model": "2f" is neural networks, "lr" is logistic regression

"save_dir": results directory

"data_dir": directory holding data

Then, run

python code/exp_run.py

This will run a lot of experiments, each of which calls audit.py. This will take a while. It runs this many trials, using the default 6 poisoning rates:

6*len(args["pois_ct"])*len(args["clip_norm"])*len(args["init_mult"])*(args["trials"][1]-args["trials"][0])

You may want to use nohup. It parallelizes on CPU.

After those experiments finish, run:

python code/run_make.py

This computes the test statistics over all models in args["save_dir"], in batches of 50. This takes some time, but is much faster than code/exp_run.py.

When run_make.py finishes, run:

python code/combine_files.py

This combines the batches of experiments. This is fast.

After that finishes, run:

python code/bkd_parser.py

This computes the lower bounds: it prints the results, but also produces a file with the results:

args["save_dir"] / results/results.npy

... which stores a dict containing a mapping from parameter settings to results.