Course Code: ppml
Duration: 14 hours
Prerequisites:
  • An understanding of machine learning principles
  • Experience with Python and ML libraries (e.g., PyTorch, TensorFlow)
  • Familiarity with data privacy or cybersecurity concepts is helpful

Audience

  • AI researchers
  • Data protection and privacy compliance teams
  • Security engineers working in regulated industries
Overview:

Privacy-Preserving Machine Learning is a field focused on protecting sensitive data while still enabling advanced AI capabilities across decentralized or restricted environments.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to implement and evaluate techniques such as federated learning, secure multiparty computation, homomorphic encryption, and differential privacy in real-world machine learning pipelines.

By the end of this training, participants will be able to:

  • Understand and compare key privacy-preserving techniques in ML.
  • Implement federated learning systems using open-source frameworks.
  • Apply differential privacy for safe data sharing and model training.
  • Use encryption and secure computation techniques to protect model inputs and outputs.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
Course Outline:

Introduction to Privacy-Preserving ML

  • Motivations and risks in sensitive data environments
  • Overview of privacy-preserving ML techniques
  • Threat models and regulatory considerations (e.g., GDPR, HIPAA)

Federated Learning

  • Concept and architecture of federated learning
  • Client-server synchronization and aggregation
  • Implementation using PySyft and Flower

Differential Privacy

  • Mathematics of differential privacy
  • Applying DP in data queries and model training
  • Using Opacus and TensorFlow Privacy

Secure Multiparty Computation (SMPC)

  • SMPC protocols and use cases
  • Encryption-based vs secret-sharing approaches
  • Secure computation workflows with CrypTen or PySyft

Homomorphic Encryption

  • Fully vs partially homomorphic encryption
  • Encrypted inference for sensitive workloads
  • Hands-on with TenSEAL and Microsoft SEAL

Applications and Industry Case Studies

  • Privacy in healthcare: federated learning for medical AI
  • Secure collaboration in finance: risk models and compliance
  • Defense and government use cases

Summary and Next Steps

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