Say goodbye to being hindered by sensitive data.
Antigranular is your platform to learn, explore, and sharpen your data privacy skills. Unlock the full potential of world's most impactful data, such as personal, financial, and trade secrets, while maintaining utmost privacy.
PETs: Empowering you with unbreakable privacy.
Antigranular is a community-driven, open-source platform that merges confidential computing and differential privacy. This creates a secure environment for handling unseen confidential data.
Prove you can be a data science master, even with eyes closed.
Start leveraging the Antigranular library now, with seamless integration into any Jupyter Notebook environment. Showcase your abilities and compete for the best accuracy models without compromising privacy.
Say goodbye to being hindered by sensitive data.
Antigranular is your platform to learn, explore, and sharpen your data privacy skills. Unlock the full potential of world's most impactful data, such as personal, financial, and trade secrets, while maintaining utmost privacy.
Say goodbye to being hindered by sensitive data.
Antigranular is your platform to learn, explore, and sharpen your data privacy skills. Unlock the full potential of world's most impactful data, such as personal, financial, and trade secrets, while maintaining utmost privacy.
Say goodbye to being hindered by sensitive data.
Antigranular is your platform to learn, explore, and sharpen your data privacy skills. Unlock the full potential of world's most impactful data, such as personal, financial, and trade secrets, while maintaining utmost privacy.
Make privacy become %%Magic
Toggle between confidential compute and regular code blocks with only four characters. Available from all notebooks:
Learn about confidential compute
From privacy violations to native errors
It may look and feel like standard Python, but any attempt to reveal sensitive data will raise a firm exception.
Learn about Private Python
Supporting the best in class disclosure control frameworks
Only differentially private results can be printed or exported back to your local session. Get started by using any of the most popular packages:
OpenDPSmartNoiseDiffPrivLib
Learn more about differential privacy
The quickest of quick Starts
Quick and easy to install from within Jupyter notebooks.
Tested on all major operating systems.
Make privacy become %%Magic
Toggle between confidential compute and regular code blocks with only four characters. Available from all notebooks:
Learn about confidential computeFrom privacy violations to native errors
It may look and feel like standard Python, but any attempt to reveal sensitive data will raise a firm exception.
Learn about Private PythonSupporting the best in class disclosure control frameworks
Only differentially private results can be printed or exported back to your local session. Get started by using any of the most popular packages:
OpenDPSmartNoiseDiffPrivLib
Learn more about differential privacyThe quickest of quick Starts
Quick and easy to install from within Jupyter notebooks.
Tested on all major operating systems.
"The world's most impactful data sits behind closed doors. Privacy technologies hold the key to unlocking its potential.Step into the forefront of data science, where you can experience algorithmic transparency while ensuring data processing opacity and safeguarding against the reverse engineering of sensitive data sources.I want to know more