Sentiment Analysis on Encrypted Data with Homomorphic Encryption
📰 Hugging Face Blog
Perform sentiment analysis on encrypted data using homomorphic encryption with the Concrete-ML library
Action Steps
- Setup the environment with Concrete-ML library
- Use a public dataset for sentiment analysis
- Represent text using a transformer
- Classify with XGBoost
- Predict over encrypted data with Concrete-ML
- Deploy the model to the cloud
Who Needs to Know This
Data scientists and machine learning engineers can benefit from this tutorial to build privacy-preserving sentiment analysis models, while software engineers and devops teams can learn about deployment to the cloud
Key Insight
💡 Homomorphic encryption enables computation on encrypted data without decryption, making it suitable for privacy-sensitive applications
Share This
🔒 Perform sentiment analysis on encrypted data with Concrete-ML library! #homomorphicencryption #sentimentanalysis
Key Takeaways
Perform sentiment analysis on encrypted data using homomorphic encryption with the Concrete-ML library
Full Article
Published Time: 2022-11-17T00:00:00.146Z
# Sentiment Analysis on Encrypted Data with Homomorphic Encryption
[Hugging Face](https://huggingface.co/)
* [Models](https://huggingface.co/models)
* [Datasets](https://huggingface.co/datasets)
* [Spaces](https://huggingface.co/spaces)
* [Buckets new](https://huggingface.co/storage)
* [Docs](https://huggingface.co/docs)
* [Enterprise](https://huggingface.co/enterprise)
* [Pricing](https://huggingface.co/pricing)
*
*
* * *
* [Log In](https://huggingface.co/login)
* [Sign Up](https://huggingface.co/join)
[Back to Articles](https://huggingface.co/blog)
# [](https://huggingface.co/blog/sentiment-analysis-fhe#sentiment-analysis-on-encrypted-data-with-homomorphic-encryption) Sentiment Analysis on Encrypted Data with Homomorphic Encryption
Published November 17, 2022
[Update on GitHub](https://github.com/huggingface/blog/blob/main/sentiment-analysis-fhe.md)
[- [x] Upvote 3](https://huggingface.co/login?next=%2Fblog%2Fsentiment-analysis-fhe)
* [](https://huggingface.co/Csplk "Csplk")
* [](https://huggingface.co/binoua "binoua")
* [](https://huggingface.co/bhosalemahesh899 "bhosalemahesh899")
[](https://huggingface.co/jfrery-zama)
[Jordan Frery jfrery-zama Follow](https://huggingface.co/jfrery-zama)
guest
* [Setup the environment](https://huggingface.co/blog/sentiment-analysis-fhe#setup-the-environment "Setup the environment")
* [Using a public dataset](https://huggingface.co/blog/sentiment-analysis-fhe#using-a-public-dataset "Using a public dataset")
* [Text representation using a transformer](https://huggingface.co/blog/sentiment-analysis-fhe#text-representation-using-a-transformer "Text representation using a transformer")
* [Classifying with XGBoost](https://huggingface.co/blog/sentiment-analysis-fhe#classifying-with-xgboost "Classifying with XGBoost")
* [Predicting over encrypted data](https://huggingface.co/blog/sentiment-analysis-fhe#predicting-over-encrypted-data "Predicting over encrypted data")
* [Deployment](https://huggingface.co/blog/sentiment-analysis-fhe#deployment "Deployment")
* [Full example in a Hugging Face Space](https://huggingface.co/blog/sentiment-analysis-fhe#full-example-in-a-hugging-face-space "Full example in a Hugging Face Space")
* [Conclusion](https://huggingface.co/blog/sentiment-analysis-fhe#conclusion "Conclusion")
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](https://github.com/zama-ai/concrete-ml), 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](https://docs.zama.ai/concrete-ml/getting-star
# Sentiment Analysis on Encrypted Data with Homomorphic Encryption
[Hugging Face](https://huggingface.co/)
* [Models](https://huggingface.co/models)
* [Datasets](https://huggingface.co/datasets)
* [Spaces](https://huggingface.co/spaces)
* [Buckets new](https://huggingface.co/storage)
* [Docs](https://huggingface.co/docs)
* [Enterprise](https://huggingface.co/enterprise)
* [Pricing](https://huggingface.co/pricing)
*
*
* * *
* [Log In](https://huggingface.co/login)
* [Sign Up](https://huggingface.co/join)
[Back to Articles](https://huggingface.co/blog)
# [](https://huggingface.co/blog/sentiment-analysis-fhe#sentiment-analysis-on-encrypted-data-with-homomorphic-encryption) Sentiment Analysis on Encrypted Data with Homomorphic Encryption
Published November 17, 2022
[Update on GitHub](https://github.com/huggingface/blog/blob/main/sentiment-analysis-fhe.md)
[- [x] Upvote 3](https://huggingface.co/login?next=%2Fblog%2Fsentiment-analysis-fhe)
* [](https://huggingface.co/Csplk "Csplk")
* [](https://huggingface.co/binoua "binoua")
* [](https://huggingface.co/bhosalemahesh899 "bhosalemahesh899")
[](https://huggingface.co/jfrery-zama)
[Jordan Frery jfrery-zama Follow](https://huggingface.co/jfrery-zama)
guest
* [Setup the environment](https://huggingface.co/blog/sentiment-analysis-fhe#setup-the-environment "Setup the environment")
* [Using a public dataset](https://huggingface.co/blog/sentiment-analysis-fhe#using-a-public-dataset "Using a public dataset")
* [Text representation using a transformer](https://huggingface.co/blog/sentiment-analysis-fhe#text-representation-using-a-transformer "Text representation using a transformer")
* [Classifying with XGBoost](https://huggingface.co/blog/sentiment-analysis-fhe#classifying-with-xgboost "Classifying with XGBoost")
* [Predicting over encrypted data](https://huggingface.co/blog/sentiment-analysis-fhe#predicting-over-encrypted-data "Predicting over encrypted data")
* [Deployment](https://huggingface.co/blog/sentiment-analysis-fhe#deployment "Deployment")
* [Full example in a Hugging Face Space](https://huggingface.co/blog/sentiment-analysis-fhe#full-example-in-a-hugging-face-space "Full example in a Hugging Face Space")
* [Conclusion](https://huggingface.co/blog/sentiment-analysis-fhe#conclusion "Conclusion")
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](https://github.com/zama-ai/concrete-ml), 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](https://docs.zama.ai/concrete-ml/getting-star
DeepCamp AI