Sentiment Analysis on Encrypted Data with Homomorphic Encryption

It is well-known that a sentiment analysis model determines whether a text is positive, negative, or neutral. However, this process typically requires access to unencrypted text, which can pose privacy concerns.

Homomorphic encryption is a type of encryption that allows for computation on encrypted data without needing to decrypt it first. This makes it well-suited for applications where user's personal and potentially sensitive data is at risk (e.g. sentiment analysis of private messages).

This blog post uses the Concrete-ML library, allowing data scientists to use machine learning models in fully homomorphic encryption (FHE) settings without any prior knowledge of cryptography. We provide a practical tutorial on how to use the library to build a sentiment analysis model on encrypted data.

The post covers:

• transformers
• how to use transformers with XGBoost to perform sentiment analysis
• how to do the training
• how to use Concrete-ML to turn predictions into predictions over encrypted data
• how to deploy to the cloud using a client/server protocol

Last but not least, we’ll finish with a complete demo over Hugging Face Spaces to show this functionality in action.

Setup the environment

First make sure your pip and setuptools are up to date by running:

pip install -U pip setuptools

Now we can install all the required libraries for the this blog with the following command.

pip install concrete-ml transformers datasets

Using a public dataset

The dataset we use in this notebook can be found here.

To represent the text for sentiment analysis, we chose to use a transformer hidden representation as it yields high accuracy for the final model in a very efficient way. For a comparison of this representation set against a more common procedure like the TF-IDF approach, please see this full notebook.

We can start by opening the dataset and visualize some statistics.

The output, then, looks like this:

Text representation using a transformer

Classifying with XGBoost

The output is as follows: