Tutorial: Deploying Trained Scikit-learn Models on Encrypted Data

If you’re interested in deploying trained Scikit-learn models on encrypted data, then this tutorial is for you. We’ll be using Fully Homomorphic Encryption (FHE) and Concrete ML to achieve this. FHE allows us to perform computations on encrypted data without decrypting it, and Concrete ML provides a framework for deploying machine learning models on encrypted data. Let’s get started!

Step 1: Train a Scikit-learn Model

The first step is to train a machine learning model using Scikit-learn. This could be a classification, regression, or clustering model. Once the model is trained, we can move on to the next step.

Step 2: Encrypt the Data

Next, we need to encrypt the data that the model will be operating on. This is where FHE comes in. We can use FHE to encrypt the data in such a way that computations can still be performed on it without decrypting it. This ensures that the data remains secure throughout the process.

Step 3: Deploy the Model Using Concrete ML

Now that the model is trained and the data is encrypted, we can deploy the model using Concrete ML. This framework allows us to execute the trained model on the encrypted data, ensuring that the privacy of the data is maintained at all times.

Step 4: Perform Inference on Encrypted Data

With the model deployed using Concrete ML, we can now perform inference on the encrypted data. This means that the model can make predictions or perform other tasks without ever decrypting the data. This is a powerful capability that enables us to maintain data privacy while still utilizing the trained model.

Conclusion

Deploying trained Scikit-learn models on encrypted data is a powerful capability that allows us to leverage machine learning while still preserving data privacy. By using FHE and Concrete ML, we can ensure that the data remains secure throughout the entire process. We hope this tutorial has been helpful in showcasing how this can be achieved.