A Light-Weight imperative language for developing provably privacy-preserving algorithms. More details can be found in the paper.
This project presents a tool for transforming LightDP programs into Python programs along with differential-privacy proofs, it is built to learn the ideas of LightDP and very primitive.
Virtual environment is highly recommended (or if you prefer virtualenv or conda, the setup is similar):
python -m venv venv
source venv/bin/acitvate
# now we're in virtual environment
pip install .usage: lightdp [-h] [-o OUT] [-c CONSTRAINTS] FILE
positional arguments:
FILE
optional arguments:
-h, --help show this help message and exit
-o OUT, --out OUT The output file name.
-c CONSTRAINTS, --constraints CONSTRAINTS
Output all constraints to file.For example, run lightdp ./examples/sparsevector.py for verifying Sparse Vector, LightDP will output:
2019-04-23 10:58:35 INFO Type check succeeds, now transforming the algorithm...
2019-04-23 10:58:35 INFO Transformation finished at ./sparse_vector_t.py. See ./sparse_vector_constraints.txt for constraints generated.
We also put some annotated programs in examples/ for references. The default name for transformed file and constraint file is *_t.py and *_constraints.txt.
MIT.