"""A gradio app for credit card approval prediction using FHE.""" import subprocess import time import gradio as gr from settings import ( REPO_DIR, ACCOUNT_MIN_MAX, CHILDREN_MIN_MAX, INCOME_MIN_MAX, AGE_MIN_MAX, FAMILY_MIN_MAX, INCOME_TYPES, OCCUPATION_TYPES, HOUSING_TYPES, EDUCATION_TYPES, FAMILY_STATUS, YEARS_EMPLOYED_BINS, INCOME_VALUE, AGE_VALUE, ) from backend import ( keygen_send, pre_process_encrypt_send_applicant, pre_process_encrypt_send_bank, pre_process_encrypt_send_credit_bureau, run_fhe, get_output_and_decrypt, explain_encrypt_run_decrypt, ) subprocess.Popen(["uvicorn", "server:app"], cwd=REPO_DIR) time.sleep(3) demo = gr.Blocks() print("Starting the demo...") with demo: gr.Markdown( """

Encrypted Credit Card Approval Prediction Using Fully Homomorphic Encryption

Concrete-ML — Documentation — Community — @zama_fhe

""" ) gr.Markdown("# Applicant, Bank and Credit bureau setup") gr.Markdown("## Step 1: Generate the keys.") gr.Markdown( """ - The private key is generated jointly by the entities that collaborate to compute the credit score. It is used to encrypt and decrypt the data and shall never be shared with any other party. - The evaluation key is a public key that the server needs to process encrypted data. It is therefore transmitted to the server for further processing as well. """ ) keygen_button = gr.Button("Generate the keys and send evaluation key to the server.") evaluation_key = gr.Textbox( label="Evaluation key representation:", max_lines=2, interactive=False ) client_id = gr.Textbox(label="", max_lines=2, interactive=False, visible=False) # Button generate the keys keygen_button.click( keygen_send, outputs=[client_id, evaluation_key, keygen_button], ) gr.Markdown("## Step 2: Fill in some information.") gr.Markdown( """ Select the information that corresponds to the profile you want to evaluate. Three sources of information are represented in this model: - the applicant's personal information in order to evaluate his/her credit card eligibility; - the applicant bank account history, which provides any type of information on the applicant's banking information relevant to the decision (here, we consider duration of account); - and credit bureau information, which represents any other information (here, employment history) that could provide additional insight relevant to the decision. Please always encrypt and send the values (through the buttons on the right) once updated before running the FHE inference. """ ) with gr.Row(): with gr.Column(): gr.Markdown("### Applicant information") bool_inputs = gr.CheckboxGroup( ["Car", "Property", "Mobile phone"], label="Which of the following do you actively hold or own?" ) num_children = gr.Slider( **CHILDREN_MIN_MAX, step=1, label="Number of children", info="How many children do you have ?" ) household_size = gr.Slider( **FAMILY_MIN_MAX, step=1, label="Household size", info="How many members does your household have ?" ) total_income = gr.Slider( **INCOME_MIN_MAX, value=INCOME_VALUE, label="Income", info="What's you total yearly income (in euros) ?" ) age = gr.Slider( **AGE_MIN_MAX, value=AGE_VALUE, step=1, label="Age", info="How old are you ?" ) with gr.Column(): income_type = gr.Dropdown( choices=INCOME_TYPES, value=INCOME_TYPES[0], label="Income type", info="What is your main type of income ?" ) education_type = gr.Dropdown( choices=EDUCATION_TYPES, value=EDUCATION_TYPES[0], label="Education", info="What is your education background ?" ) family_status = gr.Dropdown( choices=FAMILY_STATUS, value=FAMILY_STATUS[0], label="Family", info="What is your family status ?" ) occupation_type = gr.Dropdown( choices=OCCUPATION_TYPES, value=OCCUPATION_TYPES[0], label="Occupation", info="What is your main occupation ?" ) housing_type = gr.Dropdown( choices=HOUSING_TYPES, value=HOUSING_TYPES[0], label="Housing", info="In what type of housing do you live ?" ) with gr.Column(): 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 ) with gr.Row(): with gr.Column(scale=2): gr.Markdown("### Bank information") account_age = gr.Slider( **ACCOUNT_MIN_MAX, step=1, label="Account age (months)", info="How long have this person had this bank account (in months) ?" ) with gr.Column(): encrypt_button_bank = gr.Button("Encrypt the inputs and send to server.") encrypted_input_bank = gr.Textbox( label="Encrypted input representation:", max_lines=2, interactive=False ) with gr.Row(): with gr.Column(scale=2): gr.Markdown("### Credit bureau information ") employed = gr.Radio(["Yes", "No"], label="Is the person employed ?", value="Yes") years_employed = gr.Dropdown( choices=YEARS_EMPLOYED_BINS, value=YEARS_EMPLOYED_BINS[0], label="Years of employment", info="How long have this person been employed (in years) ?" ) with gr.Column(): encrypt_button_credit_bureau = gr.Button("Encrypt the inputs and send to server.") encrypted_input_credit_bureau = gr.Textbox( label="Encrypted input representation:", max_lines=2, interactive=False ) # Button to pre-process, generate the key, encrypt and send the applicant inputs from the client # side to the server encrypt_button_applicant.click( pre_process_encrypt_send_applicant, inputs=[client_id, bool_inputs, num_children, household_size, total_income, age, \ income_type, education_type, family_status, occupation_type, housing_type], outputs=[encrypted_input_applicant], ) # Button to pre-process, generate the key, encrypt and send the bank inputs from the client # side to the server encrypt_button_bank.click( pre_process_encrypt_send_bank, inputs=[client_id, account_age], outputs=[encrypted_input_bank], ) # Button to pre-process, generate the key, encrypt and send the credit bureau inputs from the # client side to the server encrypt_button_credit_bureau.click( pre_process_encrypt_send_credit_bureau, inputs=[client_id, years_employed, employed], outputs=[encrypted_input_credit_bureau], ) gr.Markdown("# Server side") gr.Markdown( """ Once the server receives the encrypted inputs, it can compute the prediction without ever needing to decrypt any value. This server employs an [XGBoost](https://github.com/dmlc/xgboost) classifier model that has been trained on a synthetic data-set. """ ) gr.Markdown("## Step 4: Run FHE execution.") execute_fhe_button = gr.Button("Run FHE execution.") fhe_execution_time = gr.Textbox( label="Total FHE execution time (in seconds):", max_lines=1, interactive=False ) # Button to send the encodings to the server using post method execute_fhe_button.click(run_fhe, inputs=[client_id], outputs=[fhe_execution_time]) gr.Markdown("# Applicant, Bank and Credit bureau decryption") gr.Markdown( """ Once the server completed the inference, the encrypted output is returned to the applicant. The three entities that provide the information to compute the credit score are the only ones that can decrypt the result. They take part in a decryption protocol that allows to only decrypt the full result when all three parties decrypt their share of the result. """ ) gr.Markdown("## Step 5: Receive the encrypted output from the server and decrypt.") gr.Markdown( """ The first value displayed below is a shortened byte representation of the actual encrypted output. The applicant is then able to decrypt the value using its private key. """ ) get_output_button = gr.Button("Receive the encrypted output from the server.") encrypted_output_representation = gr.Textbox( label="Encrypted output representation: ", max_lines=2, interactive=False ) prediction_output = gr.Textbox( label="Prediction", max_lines=1, interactive=False ) # Button to send the encodings to the server using post method get_output_button.click( get_output_and_decrypt, inputs=[client_id], outputs=[prediction_output, encrypted_output_representation], ) gr.Markdown("## Step 6 (optional): Explain the prediction.") gr.Markdown( """ In case the credit card is likely to be denied, the applicant can ask for how many years of employment would most likely be required in order to increase the chance of getting a credit card approval. All of the above steps are combined into a single button for simplicity. The following button therefore encrypts the same inputs (except the years of employment, which varies) from all three parties, runs the new prediction in FHE and decrypts the output. In case the following states to try a new "Years of employment" input, one can simply update the value in Step 2 and directly run Step 6 once more. """ ) explain_button = gr.Button( "Encrypt the inputs, compute in FHE and decrypt the output." ) explain_prediction = gr.Textbox( label="Additional years of employed required.", interactive=False ) # Button to explain the prediction explain_button.click( explain_encrypt_run_decrypt, inputs=[client_id, prediction_output, years_employed, employed], outputs=[explain_prediction], ) 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 ⭐." ) demo.launch(share=False)