datasig 0.0.1

Dataset fingerprinting library

Dataset fingerprinting

This repository contains our proof-of-concept for fingerprinting a dataset.

Local installation

git clone https://github.com/trailofbits/datasig && cd datasig
uv sync

Usage

Fingerprinting

The code below shows experimental usage of the library. This will be subject to frequent changes in early development stages.

from torchvision.datasets import MNIST
from datasig.dataset import TorchVisionDataset, CanonicalDataset

torch_dataset = MNIST(root="/tmp/data", train=True, download=True)
dataset = TorchVisionDataset(torch_dataset)
canonical = CanonicalDataset(dataset)
print("Dataset UID: ", canonical.uid)
print("Dataset fingerprint: ", canonical.fingerprint)

Dynamic dataset streaming

Datasig can be used in AIBOM solutions that require data to be streamed dynamically. Here is how to use it, using the MNIST example

On the server side (acquire and serve data):

from torchvision.datasets import MNIST
from datasig.dataset import TorchVisionDataset, CanonicalDataset
from datasig.streaming import StreamedDataset

# Create dataset and wrap with StreamedDataset
torch_dataset = MNIST(root="/tmp/data", train=True, download=True)
stream = StreamedDataset(TorchVisionDataset(torch_dataset))

# Option 1: Iterative data point access
for data_point in stream:
    pass

# Option 2: Index-based data point access
data_point = stream[10]

# Serializing data points to bytes and serve
x = stream.serialize_data_point(data_point)
your_send_data_function(x)

# Once done streaming, compute fingerprint
canonical = CanonicalDataset(stream)
print("Dataset fingerprint: ", canonical.fingerprint)

On the client side (receive, deserialize, and use the data):

from datasig.dataset import TorchVisionDataset

# Get data sent by the server side
data: bytes = your_receive_data_function()

# Deserialize data to get proper python object
data_point = TorchVisionDataset.deserialize_data_point(data)

# Do something with the data...

Development

Unit tests

Tests are in the datasig/test directory. You can run the tests with:

uv run python -m pytest # Run all tests
uv run python -m pytest -s datasig/test/test_csv.py # Run only one test file
uv run python -m pytest -s datasig/test/test_csv.py -k test_similarity # Run only one specific test function

Profiling

The profiling script generates a profile for dataset processing and fingerprint generation using cProfile. To profile the MNIST dataset from the torch framework, you can run:

uv run python profiling.py torch_mnist --full

The --full argument tells the script to include dataset canonization, UID generation, and fingerprint generation in the profile. If you want to profile only some of these steps you can cherry pick by using or omitting the following arguments instead:

uv run python profiling.py torch_mnist --canonical --uid --fingerprint

You can optionally specify the datasig config version to use (at the time of writing we have only v0) with:

uv run python profiling.py torch_mnist -v 0 --all

Currently we support only one target dataset: torch_mnist. To add another dataset, add a class in profiling.py similar to TorchMNISTV0, that implements the _setup() method which is responsible for loading the dataset.

Benchmarking

Datasig has a built-in benchmark module that allows to run experiments to benchmark speed and accuracy of various fingerprinting methods with varying configurations and on several datasets.

Benchmarks are configured programmatically using the datasig library directly. The benchmarking.py script gives a comprehensive overview of how to configure and run a benchmark, export results, as well as plot them on graph.

You can run the example benchmark with

uv run python benchmarking.py