6.3 Credit Score Calculation

Using anonymized, differential privacy-protected data to calculate credit scores.

Algorithm Architecture:

Anonymization: Anonymize the raw individual data, e.g., by de-identifying or generalizing it. Ensure that the individuals in the data are not identifiable after anonymization.

Noise Injection:

Introduce differential privacy-protected noise into the anonymized data. The amount of noise introduced needs to be adjusted based on the differential privacy parameters (such as ε value) and the sensitivity of the credit scoring model.

Credit Score Model Training:

Train the credit scoring model using anonymized data with noise. This can be a machine learning model, and the code is as follows:

python
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from sklearn.linear_model import LogisticRegression

def train_credit_score_model(data_with_noise, labels):
    model = LogisticRegression()
    model.fit(data_with_noise, labels)
return model

Credit Score Computing: Using a trained credit scoring model, calculate scores for new anonymized data. At this stage, it is necessary to introduce a certain level of noise to the input data as well.

python
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def compute_credit_score(model, new_data_with_noise):
    credit_score = model.predict_proba(new_data_with_noise)[:, 1]
    return credit_score

Credit Score Output:

Output differential privacy-protected credit score results.

Code Example:

python
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import numpy as np
from sklearn.linear_model import LogisticRegression

def anonymize_data(original_data):

    return original_data + np.random.laplace(0, 1, original_data.shape)

def train_credit_score_model(data_with_noise, labels):
    model = LogisticRegression()
    model.fit(data_with_noise, labels)
    return model

def compute_credit_score(model, new_data_with_noise):
    credit_score = model.predict_proba(new_data_with_noise)[:, 1]
    return credit_score

# Sample Data
original_data = np.random.rand(100, 5)  # Suppose 100 samples, 5 features
labels = np.random.randint(2, size=100)  # Randomly generate binary labels

# Anonymize Data
data_with_noise = anonymize_data(original_data)

# Train Credit Score Model
credit_score_model = train_credit_score_model(data_with_noise, labels)

# Anonymize New Data
new_data = np.random.rand(1, 5)  # Suppose one new data
new_data_with_noise = anonymize_data(new_data)

# Compute Credit Score
credit_score = compute_credit_score(credit_score_model, new_data_with_noise)

# Output Credit Score
print("Computed Credit Score:", credit_score)