example-1-la-place-algorithm-differential-privacy

example-1-la-place-algorithm-differential-privacy.py

import opendp as dp
import pandas as pd
import numpy as np

# Create sample customer dataset
customers = pd.DataFrame({
    'age': [25, 34, 45, 29, 52, 38, 41, 33, 47, 36],
    'purchase_amount': [150, 320, 890, 200, 450, 275, 680, 180, 520, 340]
})

# Configure privacy parameters
epsilon = 1.0  # Privacy budget
sensitivity = 1  # For count queries

# Create differentially private count query
def private_count(data, epsilon, sensitivity):
    true_count = len(data)
    noise_scale = sensitivity / epsilon
    noise = np.random.laplace(0, noise_scale)
    private_result = true_count + noise
    return max(0, round(private_result))

# Execute private count query
true_customer_count = len(customers)
private_customer_count = private_count(customers, epsilon, sensitivity)

print(f"True count: {true_customer_count}")
print(f"Private count: {private_customer_count}")
print(f"Privacy budget consumed: {epsilon}")