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--- |
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title: Sentiment Analysis on Encrypted Data Using Fully Homomorphic Encryption |
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emoji: 🥷💬 |
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colorFrom: yellow |
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colorTo: yellow |
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sdk: gradio |
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sdk_version: 3.2 |
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app_file: app.py |
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pinned: true |
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tags: [FHE, PPML, privacy, privacy preserving machine learning, homomorphic encryption, security] |
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python_version: 3.10.11 |
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--- |
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# Sentiment Analysis With FHE |
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## Set up the app locally |
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- First, create a virtual env and activate it: |
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```bash |
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python3 -m venv .venv |
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source .venv/bin/activate |
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``` |
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- Then, install required packages: |
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```bash |
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pip3 install pip --upgrade |
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pip3 install -U pip wheel setuptools --ignore-installed |
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pip3 install -r requirements.txt --ignore-installed |
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``` |
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- (optional) Compile the FHE algorithm: |
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```bash |
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python3 compile.py |
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``` |
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Check it finish well (with a "Done!"). Please note that the actual model initialization and training |
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can be found in the [SentimentClassification notebook](SentimentClassification.ipynb) (see below). |
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### Launch the app locally |
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- In a terminal: |
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```bash |
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source .venv/bin/activate |
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python3 app.py |
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``` |
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## Interact with the application |
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Open the given URL link (search for a line like `Running on local URL: http://127.0.0.1:8888/` in the |
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terminal). |
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## Train a new model |
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The notebook [SentimentClassification notebook](SentimentClassification.ipynb) provides a way to |
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train a new model. Be aware that the data needs to be downloaded beforehand using the |
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[download_data.sh](download_data.sh) file (which requires Kaggle CLI). |
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Alternatively, the dataset can be downloaded manually at |
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https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment |
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```bash |
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bash download_data.sh |
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``` |
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