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.https://github.com/huggingface/blog/blob/main/sentiment-analysis-fhe.md
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.https://github.com/huggingface/blog/blob/main/sentiment-analysis-fhe.md
GitHub
blog/sentiment-analysis-fhe.md at main · huggingface/blog
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