| import gradio as gr |
| from predictor import predict, key_already_generated, pre_process_encrypt_send_purchase, decrypt_prediction |
| import base64 |
|
|
| def key_generated(): |
| """ |
| Check if the evaluation keys have already been generated. |
| Returns: |
| bool: True if the evaluation keys have already been generated, False otherwise. |
| """ |
| if not key_already_generated(): |
| error_message = ( |
| f"Error Encountered While generating the evaluation keys." |
| ) |
| print(error_message) |
| return {gen_key_btn: gr.update(value=error_message)} |
| else: |
| print("Keys have been generated ✅") |
| return {gen_key_btn: gr.update(value="Keys have been generated ✅")} |
|
|
| |
| demo = gr.Blocks(css=".markdown-body { font-size: 18px; }") |
|
|
| with demo: |
| gr.Markdown( |
| f""" |
| <div style="display: flex; justify-content: center; align-items: center;"> |
| <img style="margin-right: 50px;" width=200 src="https://huggingface.co/spaces/Tenefix/private-fhe-fraud-detection/resolve/main/Img/zama.png"> |
| <img width=200 src="https://huggingface.co/spaces/Tenefix/private-fhe-fraud-detection/resolve/main/Img/Epita.png"> |
| </div> |
| """ |
| ) |
|
|
|
|
|
|
| gr.Markdown( |
| """ |
| <h1 style="text-align: center;">Fraud Detection with FHE Model</h1> |
| <p align="center"> |
| <a href="https://github.com/CirSandro/private-fhe-fraud-detection"> |
| <span style="vertical-align: middle; display:inline-block; margin-right: 3px;">💳</span>private-fhe-fraud-detection |
| </a> |
| — |
| <a href="https://docs.zama.ai/concrete-ml"> |
| <span style="vertical-align: middle; display:inline-block; margin-right: 3px;">🔒</span>Documentation Concrete-ML |
| </a> |
| </p> |
| """ |
| ) |
|
|
| gr.Markdown( |
| """ |
| <p align="center" style="font-size: 16px;"> |
| How to detect bank fraud without using your personal data ?</p> |
| """ |
| ) |
|
|
| with gr.Accordion("What is bank fraud detection?", open=False): |
| gr.Markdown( |
| """ |
| Bank fraud detection is the process of identifying fraudulent activities or transactions |
| that may pose a risk to a bank or its customers. It is essential to detect fraudulent |
| activities to prevent financial losses and protect the integrity of the banking system. |
| """ |
| ) |
|
|
| with gr.Accordion("Why is it important to protect this data?", open=False): |
| gr.Markdown( |
| """ |
| Banking and financial data often contain sensitive personal information, such as income, |
| spending habits, and account numbers. Protecting this information ensures that customers' |
| privacy is respected and safeguarded from unauthorized access. |
| """ |
| ) |
|
|
| with gr.Accordion("Why is Fully Homomorphic Encryption (FHE) a good solution?", open=False): |
| gr.Markdown( |
| """ |
| Fully Homomorphic Encryption (FHE) is a powerful technique for enhancing privacy and accuracy |
| in the context of fraud detection, particularly when dealing with sensitive banking data. FHE |
| allows for the encryption of data, which can then be processed and analyzed without ever needing |
| to decrypt it. |
| Each party involved in the detection process can collaborate without compromising user privacy, |
| minimizing the risk of data leaks or breaches. The data remains confidential throughout the entire |
| process, ensuring that the privacy of users is maintained. |
| """ |
| ) |
|
|
| gr.Markdown( |
| """ |
| <p style="text-align: center;"> |
| Below, we will explain the flow in the image by simulating a purchase you've just made, and show you how our fraud detection model processes the transaction. |
| </p> |
| """ |
| ) |
|
|
| gr.Markdown( |
| f""" |
| <p align="center"> |
| <img width="75%" height="30%" src="https://huggingface.co/spaces/Tenefix/private-fhe-fraud-detection/resolve/main/Img/schema.png"> |
| </p> |
| """ |
| ) |
|
|
| gr.Markdown("<hr />") |
|
|
| |
|
|
| gr.Markdown( |
| "## Step 1: Generate the keys\n\n" |
| """In Fully Homomorphic Encryption (FHE) methods, two types of keys are created. The first |
| type, called secret keys, are used to encrypt and decrypt the user's data. The second type, |
| called evaluation keys, enables a server to work on the encrypted data without seeing the |
| actual data. |
| """ |
| ) |
|
|
| gen_key_btn = gr.Button("Generate the secret and evaluation keys") |
|
|
| gen_key_btn.click( |
| key_generated, |
| inputs=[], |
| outputs=[gen_key_btn], |
| ) |
|
|
| gr.Markdown("<hr />") |
|
|
| |
|
|
| gr.Markdown( |
| "## Step 2: Make your purchase\n\n" |
| """ |
| 🛍️ It's time to shop! To simulate your latest purchase, please provide the details of your most recent transaction. |
| |
| If you don't have an idea, you can pre-fill with an example of fraud or non-fraud. |
| """ |
| ) |
|
|
| def prefill_fraud(): |
| return 34, 50, 3, False, False, False, True |
| |
| def prefill_no_fraud(): |
| return 12, 2, 0.7, True, False, True, False |
|
|
| with gr.Row(): |
| prefill_button = gr.Button("Exemple Fraud") |
| prefill_button_no = gr.Button("Exemple No-Fraud") |
|
|
| with gr.Row(): |
| with gr.Column(): |
| distance_home = gr.Number( |
| minimum=float(0), |
| maximum=float(22000), |
| step=1, |
| value=10, |
| label="Distance from Home", |
| info="How far was the purchase from your home (in km)?" |
| ) |
| distance_last = gr.Number( |
| minimum=float(0), |
| maximum=float(22000), |
| step=1, |
| value=1, |
| label="Distance from Last Transaction", |
| info="Distance between this purchase and the last one (in km)?" |
| ) |
| ratio = gr.Number( |
| minimum=float(0), |
| maximum=float(10000), |
| step=0.1, |
| value=1, |
| label="Ratio to Median Purchase Price", |
| info="Purchase ratio compared to your average purchase", |
| ) |
| repeat_retailer = gr.Checkbox( |
| label="Repeat Retailer", |
| info="Check if you are purchasing from the same retailer as your last transaction" |
| ) |
| used_chip = gr.Checkbox( |
| label="Used Chip", |
| info="Check if you used a chip card for this transaction" |
| ) |
| used_pin_number = gr.Checkbox( |
| label="Used Pin Number", |
| info="Check if you used your PIN number during the transaction" |
| ) |
| online = gr.Checkbox( |
| label="Online Order", |
| info="Check if you made your purchase online" |
| ) |
|
|
| |
| prefill_button.click( |
| fn=prefill_fraud, |
| inputs=[], |
| outputs=[ |
| distance_home, |
| distance_last, |
| ratio, |
| repeat_retailer, |
| used_chip, |
| used_pin_number, |
| online |
| ] |
| ) |
|
|
| prefill_button_no.click( |
| fn=prefill_no_fraud, |
| inputs=[], |
| outputs=[ |
| distance_home, |
| distance_last, |
| ratio, |
| repeat_retailer, |
| used_chip, |
| used_pin_number, |
| online |
| ] |
| ) |
|
|
| with gr.Row(): |
| with gr.Column(scale=2): |
| encrypt_button_applicant = gr.Button("Encrypt the inputs and send to server.") |
| |
| encrypted_input_applicant = gr.Textbox( |
| label="Encrypted input representation:", max_lines=2, interactive=False |
| ) |
|
|
| encrypt_button_applicant.click( |
| pre_process_encrypt_send_purchase, |
| inputs=[distance_home, distance_last, ratio, repeat_retailer, used_chip, used_pin_number, \ |
| online], |
| outputs=[encrypted_input_applicant, encrypt_button_applicant], |
| ) |
|
|
| gr.Markdown("<hr />") |
|
|
| |
|
|
| gr.Markdown("## Step 3: Run the FHE evaluation.") |
| gr.Markdown("<span style='color:grey'>Server Side</span>") |
| gr.Markdown( |
| """ |
| It's high time to launch our prediction, by pressing the button you will launch the |
| fraud analysis that our fictitious bank offers you. |
| This server employs a [Random Forest (by Concrete-ML)](https://github.com/zama-ai/concrete-ml/blob/release/1.8.x/docs/references/api/concrete.ml.sklearn.rf.md#class-randomforestclassifier) |
| classifier model that has been trained on a synthetic data-set. |
| """ |
| ) |
|
|
| execute_fhe_button = gr.Button("Run the FHE evaluation.") |
| fhe_execution_time = gr.Textbox( |
| label="Total FHE execution time (in seconds):", max_lines=1, interactive=False |
| ) |
|
|
| |
| execute_fhe_button.click(predict, outputs=[fhe_execution_time, execute_fhe_button]) |
|
|
| gr.Markdown("<hr />") |
|
|
| |
|
|
| gr.Markdown("## Step 4: Receive the encrypted output from the server and decrypt.") |
| gr.Markdown( |
| """ |
| 🔔 You will receive a notification! Is this a Fraud? The message is decrypted by pressing the button. |
| """ |
| ) |
|
|
| get_output_button = gr.Button("Decrypt the prediction.") |
| prediction_output = gr.Textbox( |
| label="Prediction", max_lines=1, interactive=False |
| ) |
| prediction_bar = gr.HTML(label="Prediction Bar") |
|
|
| get_output_button.click( |
| decrypt_prediction, |
| outputs=[prediction_output, get_output_button, prediction_bar], |
| ) |
| |
|
|
| gr.Markdown( |
| """ |
| You now know that it is possible to detect bank fraud without knowing your personal information. |
| """ |
| ) |
|
|
| gr.Markdown( |
| "The app was built with [Concrete-ML](https://github.com/zama-ai/concrete-ml), a " |
| "Privacy-Preserving Machine Learning (PPML) open-source set of tools by [Zama](https://zama.ai/). " |
| "Try it yourself and don't forget to star on Github ⭐." |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
