Documentation

SecureML is an open-source Python library that integrates with popular machine learning frameworks like TensorFlow and PyTorch. It provides developers with easy-to-use utilities to ensure that AI agents handle sensitive data in compliance with data protection regulations.

Key Features

• Data Anonymization Utilities:
  • K-anonymity implementation with adaptive generalization
  • Pseudonymization with format-preserving encryption
  • Configurable data masking with statistical property preservation
  • Hierarchical data generalization with taxonomy support
  • Automatic sensitive data detection
• Privacy-Preserving Training Methods:
  • Differential privacy integration with PyTorch (via Opacus) and TensorFlow (via TF Privacy)
  • Federated learning with Flower, allowing training on distributed data without centralization
  • Support for secure aggregation and privacy-preserving federated learning
• Compliance Checkers: Tools to analyze datasets and model configurations for potential privacy risks
• Synthetic Data Generation: Utilities to create synthetic datasets that mimic real data
• Regulation-Specific Presets:
  • Pre-configured YAML settings aligned with major regulations (GDPR, CCPA, HIPAA)
  • Detailed compliance requirements for each regulation
  • Customizable identifiers for personal data and sensitive information
  • Integration with compliance checking functionality
• Audit Trails and Reporting: Automatic logging of privacy measures and model decisions

Installation

Disclaimer: Due to Tensorflow-privacy compatibility issues, SecureML is only available up to Python 3.11. We will update as soon as Tensorflow-privacy releases a version compatible to Python 3.12+

With pip (Python 3.11):

pip install secureml

Optional Dependencies

# For generating PDF reports for compliance and audit trails
pip install secureml[pdf]

# For secure key management with HashiCorp Vault
pip install secureml[vault]

# For all optional components
pip install secureml[pdf,vault]

Quick Start

Data Anonymization

Anonymizing a dataset to comply with privacy regulations:

import pandas as pd
from secureml import anonymize

# Load your dataset
data = pd.DataFrame({
    "name": ["John Doe", "Jane Smith", "Bob Johnson"],
    "age": [32, 45, 28],
    "email": ["john.doe@example.com", "jane.smith@example.com", "bob.j@example.com"],
    "ssn": ["123-45-6789", "987-65-4321", "456-78-9012"],
    "zip_code": ["10001", "94107", "60601"],
    "income": [75000, 82000, 65000]
})
    
# Anonymize using k-anonymity
anonymized_data = anonymize(
    data,
    method="k-anonymity",
    k=2,
        sensitive_columns=["name", "email", "ssn"]
    )
    
    print(anonymized_data)

Compliance Checking with Regulation Presets

SecureML includes built-in presets for major regulations (GDPR, CCPA, HIPAA) that define the compliance requirements specific to each regulation:

import pandas as pd
from secureml import check_compliance
    
# Load your dataset
data = pd.read_csv("your_dataset.csv")
    
# Model configuration
model_config = {
    "model_type": "neural_network",
    "input_features": ["age", "income", "zip_code"],
    "output": "purchase_likelihood",
    "training_method": "standard_backprop"
}
    
# Check compliance with GDPR
report = check_compliance(   
    data=data,
    model_config=model_config,
    regulation="GDPR"
)
    
# View compliance issues
if report.has_issues():
    print("Compliance issues found:")
    for issue in report.issues:
        print(f"- {issue['component']}: {issue['issue']} ({issue['severity']})")
        print(f"  Recommendation: {issue['recommendation']}")

Privacy-Preserving Machine Learning

Train a model with differential privacy guarantees:

import torch.nn as nn
import pandas as pd
from secureml import differentially_private_train
    
# Create a simple PyTorch model
model = nn.Sequential(
    nn.Linear(10, 64),
    nn.ReLU(),
    nn.Linear(64, 2),
    nn.Softmax(dim=1)
)
    
# Load your dataset
data = pd.read_csv("your_dataset.csv")
    
# Train with differential privacy
private_model = differentially_private_train(
    model=model,
    data=data,
    epsilon=1.0,  # Privacy budget
    delta=1e-5,   # Privacy delta parameter
    epochs=10,
    batch_size=64
)

Synthetic Data Generation

Generate synthetic data that maintains the statistical properties of the original data:

import pandas as pd
from secureml import generate_synthetic_data
    
# Load your dataset
data = pd.read_csv("your_dataset.csv")
    
# Generate synthetic data
synthetic_data = generate_synthetic_data(
    template=data,
    num_samples=1000,
    method="statistical",  # Options: simple, statistical, sdv-copula, gan
    sensitive_columns=["name", "email", "ssn"]
)
    
print(synthetic_data.head())

Documentation

For detailed documentation, examples, and API reference, visit our documentation.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request or Issue. Our focus is expanding supported legislations beyond GDPR, CCPA, and HIPAA. You can help us with that!

License

This project is licensed under the MIT License - see the LICENSE file for details.