AI models help us every day. They make things better, from helping you find what you want to new science discoveries. The better the data, the better the AI. Good data helps AI learn well. But we must keep personal data safe. JAX and JAX-Privacy help do this.
JAX is a tool for computers to do math fast. It helps build big AI models. JAX has special features. It can learn how to change math (automatic differentiation). It can make code run faster (just-in-time compilation). It works on many computers at once. This makes building AI models quick. JAX is used by many AI experts. Other tools like Flax help build AI models. Optax helps make AI models learn better.
JAX-Privacy is built on JAX. It helps make AI models private. It lets people train AI models using private data. It has tools to add privacy to machine learning. This helps train AI models on big data sets safely. JAX-Privacy helps put private training into AI systems. The first JAX-Privacy was made in 2022. It helped others check our work on private training. Now, it is a place for teams to share new ideas for private AI.
Today, we share JAX-Privacy 1.0. This new version has our newest research. It is made to be easy to use. It helps you build private AI models. You can use JAX's speed with new private AI methods.
Why JAX-Privacy Was Made
People use private AI methods to keep data safe. These methods show how much private data might be seen. They ensure that an AI's result is almost the same. It does not matter if one person's data is in the set or not.
Using private AI in big AI models is hard. The usual way is called DP-SGD. It needs special ways to group data. It needs to make each person's data's impact smaller. It also needs to add small random numbers. This takes a lot of computer power. It can be hard to do it right and fast for big AI models.
JAX-Privacy helps people train AI models on private data. It uses private methods. It works fast and is safe. It has tools to make data smaller and add noise. This works well when many computers work together.
Other tools have tried to help. But they are not always fast or easy to change. Our work has always tried to make private AI better. We made new private AI methods. We also made ways to check how private the AI is. We needed a tool that could keep up. It needed to be fast and handle complex AI models. It also needed to work on many computers.
JAX works in a special way. It can do many things at once (vectorization). It can run the same code on many computers (parallelization). This made JAX a good base. Building on JAX, we made a tool that works on many computers easily. It can train big AI models on many fast computers. JAX-Privacy is the result. It has been used inside Google. Now, we share it with everyone.
What JAX-Privacy Does
JAX-Privacy makes private AI easier. It has many useful parts:
- Main parts: The tool has good ways to do private AI steps. This includes making each person's data impact smaller. It adds noise. It helps pick data for training. These parts help build things like DP-SGD.
- Newest methods: JAX-Privacy also has newer ways. It can do things like DP matrix math. This can make AI models work better. It lets people try new private training methods.
- Works on many computers: All parts work well with JAX's speed features. You can train big AI models on many computers. You do not need hard code. This makes training big AI models with privacy real. JAX-Privacy also helps handle very big data. This helps get the best balance for privacy and AI quality.
- Correct and checkable: The tool uses Google's private AI math library. This makes sure the privacy math is right. It helps find the best privacy settings. You can also check how private the AI is. You can test your own ways to check privacy.
JAX-Privacy has tools for making data smaller, adding noise, picking data, checking privacy, and more. You can use these tools to build private AI training.
From Ideas to Use: Train Big AI Safely
One great thing about JAX-Privacy is how it works in real life. The tool helps train big AI models. It is used for models like VaultGemma. VaultGemma is the best private AI language model. It was made using JAX-Privacy.
This new tool helps people train big AI models easily. You can use it with Keras. We have examples for training Gemma models. Gemma models are made by Google DeepMind. These examples show how to use JAX-Privacy. You can use it for tasks like making text shorter. You can also make new private data. This shows the tool works well even with the newest AI models.
JAX-Privacy makes it easy to add privacy to AI. This helps people build safe AI apps. You can train an AI for health care. Or you can train an AI for money advice. It makes private AI easier for more people. It helps make smart AI more fair.
What's Next
We are happy to share JAX-Privacy with everyone. This tool took many years to make. It is a big step for private AI. We hope it helps many new ideas. This will help everyone.
We will keep making JAX-Privacy better. We will add new research. We will listen to what people need. We want to see what you build with JAX-Privacy. See the code on GitHub or get it from PIP. Start building private AI models now.