PyDP | The Python Differential Privacy Library | Machine Learning library

PyDP is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. PyDP has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install PyDP' or download it from GitHub, PyPI.

In today's data-driven world, more and more researchers and data scientists use machine learning to create better models or more innovative solutions for a better future. These models often tend to handle sensitive or personal data, which can cause privacy issues. For example, some AI models can memorize details about the data they've been trained on and could potentially leak these details later on. To help measure sensitive data leakage and reduce the possibility of it happening, there is a mathematical framework called differential privacy. In 2020, OpenMined created a Python wrapper for Google's Differential Privacy project called PyDP. The library provides a set of ε-differentially private algorithms, which can be used to produce aggregate statistics over numeric data sets containing private or sensitive information. Therefore, with PyDP you can control the privacy guarantee and accuracy of your model written in Python.