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"""A gradio app for credit card approval prediction using FHE.""" |
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import subprocess |
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import time |
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import gradio as gr |
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from settings import ( |
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REPO_DIR, |
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ACCOUNT_MIN_MAX, |
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CHILDREN_MIN_MAX, |
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INCOME_MIN_MAX, |
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AGE_MIN_MAX, |
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FAMILY_MIN_MAX, |
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INCOME_TYPES, |
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OCCUPATION_TYPES, |
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HOUSING_TYPES, |
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EDUCATION_TYPES, |
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FAMILY_STATUS, |
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YEARS_EMPLOYED_BINS, |
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INCOME_VALUE, |
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AGE_VALUE, |
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) |
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from backend import ( |
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keygen_send, |
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pre_process_encrypt_send_applicant, |
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pre_process_encrypt_send_bank, |
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pre_process_encrypt_send_credit_bureau, |
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run_fhe, |
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get_output_and_decrypt, |
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explain_encrypt_run_decrypt, |
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) |
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subprocess.Popen(["uvicorn", "server:app"], cwd=REPO_DIR) |
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time.sleep(3) |
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demo = gr.Blocks() |
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print("Starting the demo...") |
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with demo: |
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gr.Markdown( |
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""" |
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<p align="center"> |
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<img width=200 src="https://user-images.githubusercontent.com/5758427/197816413-d9cddad3-ba38-4793-847d-120975e1da11.png"> |
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</p> |
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""" |
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) |
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gr.Markdown( |
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""" |
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<h1 align="center">Encrypted Credit Card Approval Prediction Using Fully Homomorphic Encryption</h1> |
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<p align="center"> |
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<a href="https://github.com/zama-ai/concrete-ml"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="file/images/logos/github.png">Concrete-ML</a> |
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— |
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<a href="https://docs.zama.ai/concrete-ml"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="file/images/logos/documentation.png">Documentation</a> |
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— |
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<a href="https://zama.ai/community"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="file/images/logos/community.png">Community</a> |
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— |
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<a href="https://twitter.com/zama_fhe"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="file/images/logos/x.png">@zama_fhe</a> |
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</p> |
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""" |
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) |
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with gr.Accordion("What is credit scoring for card approval?", open=False): |
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gr.Markdown( |
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""" |
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It is a complex process that involves several entities: the applicant, the bank, the |
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credit bureau, and the credit scoring agency. When you apply for a credit card, you |
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provide personal and financial information to the bank. This might include your income, |
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employment status, and existing debts. The bank uses this information to assess your |
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creditworthiness. To do this, they often turn to credit bureaus and credit scoring |
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agencies. |
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- Credit bureaus collect and maintain data on consumers' credit and payment |
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histories. This data includes your past and current debts, payment history, and the |
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length of your credit history. |
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- Credit scoring agencies use algorithms to analyze |
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the data from credit bureaus and generate a credit score. This score is a numerical |
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representation of your creditworthiness. |
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- The bank uses your credit score, along with |
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the information you provided, to make a decision on your credit card application. A |
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higher credit score generally increases your chances of being approved and may result |
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in better terms (like a lower interest rate). |
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""" |
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) |
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with gr.Accordion("Why is it critical to add a new privacy layer to this process?", open=False): |
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gr.Markdown( |
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""" |
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The data involved is highly sensitive. It includes personal details like your Social |
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Security number, income, and credit history. There's significant sharing of data |
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between different entities. Your information is not just with the bank, but also with |
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credit bureaus and scoring agencies. The more entities that have access to your data, |
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the greater the risk of a data breach. This can lead to identity theft and financial |
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fraud. There's also the issue of data accuracy. Mistakes in credit reports can lead to |
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unjustly low credit scores, affecting your ability to get credit. |
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""" |
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) |
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with gr.Accordion( |
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"Why is Fully Homomorphic Encryption (FHE) a solution for better credit scoring?", |
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open=False, |
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): |
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gr.Markdown( |
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""" |
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Fully Homomorphic Encryption (FHE) is seen as an ideal solution for enhancing privacy |
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and accuracy in credit scoring processes involving multiple parties like applicants, |
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banks, credit bureaus, and credit scoring agencies. It allows data to be encrypted and |
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processed without ever needing to decrypt it. This means that sensitive data can be |
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shared and analyzed without exposing the actual information to any of the parties or |
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the server processing it. In the context of credit scoring, this would enable a more |
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thorough and accurate assessment of a person's creditworthiness. Data from various |
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sources can be combined and analyzed to make a more informed decision, yet each party's |
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data remains confidential. As a result, the risk of data leaks or breaches is |
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significantly minimized, addressing major privacy concerns. |
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To summarize, FHE provides a means to make more accurate credit eligibility decisions |
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while maintaining strict data privacy, offering a sophisticated solution to the delicate |
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balance between data utility and confidentiality. |
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""" |
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) |
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gr.Markdown( |
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""" |
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<p align="center"> |
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<img src="https://raw.githubusercontent.com/kcelia/Img/main/credit_scoring_banner.png" |
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</p> |
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""" |
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) |
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gr.Markdown("## Step 1: Generate the keys.") |
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gr.Markdown("<hr />") |
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gr.Markdown("<span style='color:grey'>Applicant, Bank and Credit bureau setup</span>") |
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gr.Markdown( |
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""" |
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- The private key is generated jointly by the entities that collaborate to compute the |
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credit score. It is used to encrypt and decrypt the data and shall never be shared with |
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any other party. |
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- The evaluation key is a public key that the server needs to process encrypted data. It is |
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therefore transmitted to the server for further processing as well. |
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""" |
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) |
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keygen_button = gr.Button("Generate the keys and send evaluation key to the server.") |
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evaluation_key = gr.Textbox( |
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label="Evaluation key representation:", max_lines=2, interactive=False |
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) |
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client_id = gr.Textbox(label="", max_lines=2, interactive=False, visible=False) |
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keygen_button.click( |
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keygen_send, |
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outputs=[client_id, evaluation_key, keygen_button], |
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) |
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gr.Markdown("## Step 2: Fill in some information.") |
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gr.Markdown("<hr />") |
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gr.Markdown("<span style='color:grey'>Applicant, Bank and Credit bureau setup</span>") |
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gr.Markdown( |
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""" |
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Select the information that corresponds to the profile you want to evaluate. Three sources |
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of information are represented in this model: |
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- the applicant's personal information in order to evaluate his/her credit card eligibility; |
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- the applicant bank account history, which provides any type of information on the |
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applicant's banking information relevant to the decision (here, we consider duration of |
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account); |
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- and credit bureau information, which represents any other information (here, |
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employment history) that could provide additional insight relevant to the decision. |
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Please always encrypt and send the values (through the buttons on the right) once updated |
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before running the FHE inference. |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown("### Step 2.1 - Applicant information 🧑💻") |
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bool_inputs = gr.CheckboxGroup( |
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["Car", "Property", "Mobile phone"], |
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label="Which of the following do you actively hold or own?" |
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) |
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num_children = gr.Slider( |
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**CHILDREN_MIN_MAX, |
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step=1, |
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label="Number of children", |
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info="How many children do you have ?" |
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) |
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household_size = gr.Slider( |
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**FAMILY_MIN_MAX, |
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step=1, |
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label="Household size", |
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info="How many members does your household have ?" |
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) |
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total_income = gr.Slider( |
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**INCOME_MIN_MAX, |
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value=INCOME_VALUE, |
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label="Income", |
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info="What's you total yearly income (in euros) ?" |
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) |
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age = gr.Slider( |
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**AGE_MIN_MAX, |
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value=AGE_VALUE, |
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step=1, |
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label="Age", |
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info="How old are you ?" |
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) |
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with gr.Column(): |
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income_type = gr.Dropdown( |
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choices=INCOME_TYPES, |
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value=INCOME_TYPES[0], |
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label="Income type", |
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info="What is your main type of income ?" |
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) |
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education_type = gr.Dropdown( |
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choices=EDUCATION_TYPES, |
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value=EDUCATION_TYPES[0], |
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label="Education", |
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info="What is your education background ?" |
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) |
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family_status = gr.Dropdown( |
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choices=FAMILY_STATUS, |
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value=FAMILY_STATUS[0], |
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label="Family", |
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info="What is your family status ?" |
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) |
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occupation_type = gr.Dropdown( |
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choices=OCCUPATION_TYPES, |
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value=OCCUPATION_TYPES[0], |
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label="Occupation", |
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info="What is your main occupation ?" |
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) |
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housing_type = gr.Dropdown( |
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choices=HOUSING_TYPES, |
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value=HOUSING_TYPES[0], |
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label="Housing", |
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info="In what type of housing do you live ?" |
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) |
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with gr.Row(): |
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with gr.Column(scale=2): |
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encrypt_button_applicant = gr.Button("Encrypt the inputs and send to server.") |
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encrypted_input_applicant = gr.Textbox( |
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label="Encrypted input representation:", max_lines=2, interactive=False |
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) |
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gr.Markdown("<hr />") |
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with gr.Column(): |
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gr.Markdown("### Step 2.2 - Bank information 🏦") |
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account_age = gr.Slider( |
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**ACCOUNT_MIN_MAX, |
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step=1, |
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label="Account age (months)", |
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info="How long have this person had this bank account (in months) ?" |
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) |
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with gr.Row(): |
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with gr.Column(scale=2): |
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encrypt_button_bank = gr.Button("Encrypt the inputs and send to server.") |
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encrypted_input_bank = gr.Textbox( |
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label="Encrypted input representation:", max_lines=2, interactive=False |
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) |
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gr.Markdown("<hr />") |
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with gr.Column(): |
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gr.Markdown("### Step 2.3 - Credit bureau information 🏢") |
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employed = gr.Radio(["Yes", "No"], label="Is the person employed ?", value="Yes") |
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years_employed = gr.Dropdown( |
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choices=YEARS_EMPLOYED_BINS, |
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value=YEARS_EMPLOYED_BINS[0], |
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label="Years of employment", |
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info="How long have this person been employed (in years) ?" |
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) |
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with gr.Row(): |
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with gr.Column(scale=2): |
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encrypt_button_credit_bureau = gr.Button("Encrypt the inputs and send to server.") |
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encrypted_input_credit_bureau = gr.Textbox( |
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label="Encrypted input representation:", max_lines=2, interactive=False |
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) |
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encrypt_button_applicant.click( |
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pre_process_encrypt_send_applicant, |
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inputs=[client_id, bool_inputs, num_children, household_size, total_income, age, \ |
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income_type, education_type, family_status, occupation_type, housing_type], |
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outputs=[encrypted_input_applicant, encrypt_button_applicant], |
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) |
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encrypt_button_bank.click( |
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pre_process_encrypt_send_bank, |
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inputs=[client_id, account_age], |
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outputs=[encrypted_input_bank, encrypt_button_bank], |
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) |
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encrypt_button_credit_bureau.click( |
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pre_process_encrypt_send_credit_bureau, |
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inputs=[client_id, years_employed, employed], |
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outputs=[encrypted_input_credit_bureau, encrypt_button_credit_bureau], |
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) |
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gr.Markdown("## Step 3: Run the FHE evaluation.") |
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gr.Markdown("<hr />") |
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gr.Markdown("<span style='color:grey'>Server Side</span>") |
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gr.Markdown( |
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""" |
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Once the server receives the encrypted inputs, it can compute the prediction without ever |
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needing to decrypt any value. |
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This server employs a [Decision Tree](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) |
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classifier model that has been trained on a synthetic data-set. |
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""" |
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) |
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execute_fhe_button = gr.Button("Run the FHE evaluation.") |
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fhe_execution_time = gr.Textbox( |
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label="Total FHE execution time (in seconds):", max_lines=1, interactive=False |
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) |
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execute_fhe_button.click(run_fhe, inputs=[client_id], outputs=[fhe_execution_time, execute_fhe_button]) |
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gr.Markdown("## Step 4: Receive the encrypted output from the server and decrypt.") |
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gr.Markdown("<hr />") |
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gr.Markdown("<span style='color:grey'>Applicant, Bank and Credit bureau decryption</span>") |
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gr.Markdown( |
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""" |
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Once the server completed the inference, the encrypted output is returned to the applicant. |
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The three entities that provide the information to compute the credit score are the only |
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ones that can decrypt the result. They take part in a decryption protocol that allows to |
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only decrypt the full result when all three parties decrypt their share of the result. |
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""" |
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) |
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gr.Markdown( |
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""" |
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The first value displayed below is a shortened byte representation of the actual encrypted |
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output. |
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The applicant is then able to decrypt the value using its private key. |
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""" |
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) |
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get_output_button = gr.Button("Receive the encrypted output from the server.") |
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encrypted_output_representation = gr.Textbox( |
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label="Encrypted output representation: ", max_lines=2, interactive=False |
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) |
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prediction_output = gr.Textbox( |
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label="Prediction", max_lines=1, interactive=False |
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) |
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get_output_button.click( |
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get_output_and_decrypt, |
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inputs=[client_id], |
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outputs=[prediction_output, encrypted_output_representation, get_output_button], |
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) |
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gr.Markdown("## Step 5: Explain the prediction (only if your credit card is likely to be denied).") |
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gr.Markdown("<hr />") |
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gr.Markdown( |
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""" |
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In case the credit card is likely to be denied, the applicant can ask for how many years of |
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employment would most likely be required in order to increase the chance of getting a |
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credit card approval. |
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All of the above steps are combined into a single button for simplicity. The following |
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button therefore encrypts the same inputs (except the years of employment, which varies) |
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from all three parties, runs the new prediction in FHE and decrypts the output. |
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In case the following states to try a new "Years of employment" input, one can simply |
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update the value in Step 2 and directly run Step 6 once more. |
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""" |
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) |
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explain_button = gr.Button( |
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"Encrypt the inputs, compute in FHE and decrypt the output." |
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) |
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explain_prediction = gr.Textbox( |
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label="Additional years of employed required.", interactive=False |
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) |
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explain_button.click( |
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explain_encrypt_run_decrypt, |
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inputs=[client_id, prediction_output, years_employed, employed], |
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outputs=[explain_prediction, explain_button], |
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) |
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gr.Markdown( |
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"The app was built with [Concrete-ML](https://github.com/zama-ai/concrete-ml), a " |
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"Privacy-Preserving Machine Learning (PPML) open-source set of tools by [Zama](https://zama.ai/). " |
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"Try it yourself and don't forget to star on Github ⭐." |
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) |
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demo.launch(share=False) |
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