User Guide

This user guide provides detailed information on using SecureML’s features to create privacy-preserving machine learning systems.

Contents:

Overview

SecureML is designed to help machine learning engineers build privacy-preserving AI systems that comply with regulations like GDPR, CCPA, and HIPAA. The library provides tools across the entire machine learning lifecycle:

Data Preparation

  • Anonymization: Transform sensitive data to protect individual privacy while maintaining utility

  • Synthetic Data: Generate realistic but artificial data that preserves statistical properties

Model Training

  • Differential Privacy: Train models with mathematical privacy guarantees

  • Federated Learning: Train models across decentralized data sources without sharing raw data

Compliance & Auditing

  • Compliance Checking: Verify that your datasets and models comply with privacy regulations

  • Audit Trails: Maintain comprehensive logs of all data operations for compliance documentation

  • Reporting: Generate detailed reports to document compliance and audit trails

Security

  • Key Management: Securely store and manage encryption keys using HashiCorp Vault

  • Command Line Interface: Perform secure operations via a comprehensive CLI

  • Isolated Environments: Manage dependency conflicts with advanced environment isolation