机器学习中的数据隐私保护：技术和应用

    noise = laplace_mech(input) 
    output = input + noise 

    smoothed label = (count+α)/(total_count+K*α) 

    import numpy as np
    
    # Step 1: Prepare Dataset
    raw_data = get_dataset() # Get raw data from database or file system.
    train_data, test_data = split_dataset(raw_data) # Split the dataset into training and testing sets.
    
    # Step 2: Data Randomization
    randomized_data = randomize_data(train_data) # Randomize the training data.
    np.save('randomized_data', randomized_data) # Save the randomized data to a local file.
    
    # Step 3: Send Randomized Subsets to Third Parties
    for i in range(3):
    subset = create_subset(randomized_data) # Create a subset of randomized data for each party.
    save_to_file(f'subset_{i}.csv', subset) # Save the subset to a local file.
    
    # Step 4: Train Model on Each Party's Data
    models = []
    for i in range(3):
    filename = f'subset_{i}.csv'
    model = train_model(filename) # Train models on each party's subsets.
    models.append(model)
    
    # Step 5: Combine Models Predictions
    predictions = [model.predict(test_data) for model in models] # Use all three models to predict the labels.
    final_prediction = combine_predictions(predictions) # Combine predictions using majority vote or average score.
    
    print("Final Prediction:", final_prediction)

    from scipy.stats import laplace # Import Laplace distribution from SciPy library.
    
    def laplace_mech(x, eps=0.1, s=1):
    """Generate Laplace mechanism with privacy parameter epsilon."""
    
    loc = 0
    scale = float(s)/eps
    noisy_x = laplace.rvs(loc=loc, scale=scale, size=len(x))
    return x + noisy_x
    
    # Step 1: Choose Parameters
    epsilon = 0.1 # Set the privacy parameter epsilon.
    num_records = len(data) # The number of records in the data set.
    s = num_records / epsilon**2 # Calculate the sensitivity parameter.
    
    # Step 2: Add Noise to Input Data
    noisy_data = laplace_mech(data, epsilon, s) # Generate noise based on Laplace mechanism.
    
    # Step 3: Train the Model
    model = build_model(noisy_data) # Build a machine learning model based on noisy data.
    
    # Step 4: Receive Model Predictions
    clean_pred = receive_preds(model) # Receive encrypted model prediction results.
    print("Clean Predicted Labels:", clean_pred)

    from collections import Counter
    
    def smooth_labels(y, K, alpha=0.1):
    """Smooth labels by adding Laplace smoothing."""
    
    counts = dict(Counter(y))
    total_count = sum([counts[j] for j in counts])
    new_y = {}
    for i in y:
        count = counts[i] if i in counts else 0
        new_y[i] = (count+alpha)/(total_count+K*alpha)
    
    return list(new_y.values())
    
    
    # Step 1: Count Label Classes
    class_counts = dict(Counter(y))
    K = max(class_counts.values()) # Number of classes
    
    # Step 2: Smooth the Labels
    smoothed_labels = smooth_labels(y, K)
    
    # Step 3: Train the Model
    model = build_model(X, smoothed_labels)
    
    # Step 4: Output Final Result
    predicted_probs = model.predict_proba(X_test)[:,1]
    predicted_labels = np.array([int(p>0.5) for p in predicted_probs])
    predicted_scores = {idx: proba for idx, proba in enumerate(predicted_probs)}

    import syft as sy
    from sklearn.datasets import make_classification # Load classification dataset
    
    hook = sy.TorchHook(torch) # Initialize TorchHook to enable PySyft features
    
    # Step 1: Define Local Budget Function
    def compute_local_budget(model, sample_size, budget):
    """Compute local budget."""
    
    params = model.parameters()
    n_params = sum([param.numel() for param in params])
    fractional_budget = min(sample_size/n_params, budget)
    
    return int(fractional_budget)
    
    # Step 2: Set Local Budgets
    doctor_budget = 50 # Maximum samples for doctor model 
    patient_budget = 20 # Maximum samples for patient data
    admin_budget = 100 # Maximum samples for admin data
    
    # Step 3: Setup Data
    data, target = make_classification(n_samples=100, n_features=5, n_informative=3, n_redundant=1, n_classes=2)
    data /= np.max(np.abs(data), axis=0).reshape((1,-1)) # Normalize the data to have zero mean and unit variance
    
    # Step 4: Share Data with Participants
    doctor_data = torch.tensor(data[:doctor_budget]).tag("#doctor").send(participant1) # Send doctor data to participant 1
    patient_data = torch.tensor(data[doctor_budget:doctor_budget+patient_budget]).tag("#patient").send(participant2) # Send patient data to participant 2
    admin_data = torch.tensor(data[doctor_budget+patient_budget:]).tag("#admin").send(participant3) # Send admin data to participant 3
    
    # Step 5: Train Models
    doctor_model = build_doctor_model().fix_precision().share(*[participant1, participant2], crypto_provider=crypto_provider)
    patient_model = build_patient_model().fix_precision().share(*[participant2, participant3], crypto_provider=crypto_provider)
    admin_model = build_admin_model().fix_precision().share(*[participant3, participant1], crypto_provider=crypto_provider)
    
    # Step 6: Execute Training Procedures Locally
    doctor_local_budget = compute_local_budget(doctor_model, doctor_budget, patient_budget)
    doctor_train_loader = DataLoader(doctor_data, batch_size=doctor_local_budget)
    for epoch in range(10):
    for X, y in doctor_train_loader:
        optimizer.zero_grad()
        pred = doctor_model(X)
        loss = criterion(pred, y)
        loss.backward()
        optimizer.step()
    
    # Step 7: Aggregate Model Updates
    doctor_agg_update = doctor_model.get().float_prec()
    patient_agg_update = patient_model.get().float_prec()
    admin_agg_update = admin_model.get().float_prec()
    
    # Step 8: Decrypt and Evaluate Results
    aggregate_model = build_aggregate_model().fix_precision().encrypt() # Aggregate all models locally
    result = aggregate_model(doctor_agg_update, patient_agg_update, admin_agg_update) # Reconstruct global model
    accuracy = evaluate_results(result, true_labels) # Evaluate the accuracy of the aggregated model
    
    print("Accuracy of Global Model:", accuracy)