15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
Session Objectives
- Understand the fundamentals of privacy-preserving machine learning.
- Master differential privacy techniques for data anonymization.
- Utilize federated learning for decentralized model training.
- Implement secure multi-party computation for collaborative analysis.
- Design and build privacy-preserving machine learning models.
- Optimize machine learning workflows for data confidentiality.
- Troubleshoot and address common privacy challenges in ML.
- Implement data anonymization and de-identification techniques.
- Integrate privacy-preserving ML with real-world applications.
- Understand how to handle sensitive data and ensure data governance.
- Explore advanced privacy-preserving techniques (e.g., homomorphic encryption, zero-knowledge proofs).
- Apply real world use cases for privacy-preserving machine learning.
- Leverage privacy-preserving ML frameworks and tools for efficient development.
About the Course
This advanced training equips professionals with the knowledge and practical techniques required to design, develop, deploy, and govern machine learning pipelines that protect sensitive data while meeting applicable regulatory and organizational requirements.
The course combines AI governance, data protection, privacy engineering, and machine learning operations to address privacy risks throughout the ML lifecycle. Participants will explore privacy-preserving machine learning techniques, data minimization, anonymization and pseudonymization, differential privacy, secure data processing, model governance, privacy risk assessment, auditability, and responsible AI practices.
The training emphasizes practical approaches for embedding privacy and compliance controls into data preparation, model development, validation, deployment, monitoring, and retirement.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of privacy-preserving machine learning.
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Workshop 1.2:
Overview of differential privacy, federated learning, and secure multi-party computation.
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Workshop 1.3:
Setting up a privacy-preserving ML development environment.
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Workshop 1.4:
Introduction to privacy-preserving ML frameworks and tools.
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Workshop 1.5:
Best practices for privacy-preserving ML.
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Workshop 2.1:
Implementing differential privacy techniques for data anonymization.
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Workshop 2.2:
Utilizing epsilon and delta parameters for privacy guarantees.
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Workshop 2.3:
Designing and building differentially private algorithms.
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Workshop 2.4:
Optimizing differential privacy for accuracy and utility.
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Workshop 2.5:
Best practices for differential privacy.
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Workshop 3.1:
Implementing federated learning for decentralized model training.
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Workshop 3.2:
Utilizing secure aggregation and model averaging.
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Workshop 3.3:
Designing and building federated learning systems.
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Workshop 3.4:
Optimizing federated learning for communication efficiency.
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Workshop 3.5:
Best practices for federated learning.
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Workshop 4.1:
Implementing secure multi-party computation for collaborative analysis.
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Workshop 4.2:
Utilizing secret sharing and homomorphic encryption.
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Workshop 4.3:
Designing and building secure computation protocols.
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Workshop 4.4:
Optimizing SMPC for performance and security.
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Workshop 4.5:
Best practices for SMPC.
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Workshop 5.1:
Designing and building privacy-preserving machine learning models.
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Workshop 5.2:
Implementing privacy-preserving neural networks and decision trees.
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Workshop 5.3:
Utilizing privacy-preserving data augmentation techniques.
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Workshop 5.4:
Optimizing models for privacy and accuracy.
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Workshop 5.5:
Best practices for privacy-preserving models.
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Workshop 6.1:
Optimizing machine learning workflows for data confidentiality.
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Workshop 6.2:
Utilizing data anonymization and pseudonymization.
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Workshop 6.3:
Implementing data access control and audit trails.
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Workshop 6.4:
Designing secure data processing pipelines.
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Workshop 6.5:
Best practices for workflow optimization.
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Workshop 7.1:
Debugging common privacy challenges in machine learning.
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Workshop 7.2:
Analyzing privacy risks and vulnerabilities.
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Workshop 7.3:
Utilizing troubleshooting techniques for problem resolution.
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Workshop 7.4:
Resolving common privacy issues.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data anonymization and de-identification techniques.
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Workshop 8.2:
Utilizing k-anonymity and l-diversity.
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Workshop 8.3:
Designing and building anonymization pipelines.
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Workshop 8.4:
Optimizing anonymization for data utility.
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Workshop 8.5:
Best practices for anonymization.
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Workshop 9.1:
Integrating privacy-preserving ML with real-world applications.
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Workshop 9.2:
Utilizing APIs and data connectors.
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Workshop 9.3:
Implementing privacy-preserving ML in healthcare and finance.
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Workshop 9.4:
Optimizing integration for specific application domains.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Understanding how to handle sensitive data and ensure data governance.
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Workshop 10.2:
Utilizing data encryption and access control.
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Workshop 10.3:
Designing and building data governance policies.
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Workshop 10.4:
Optimizing data handling for regulatory compliance.
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Workshop 10.5:
Best practices for data governance.
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Workshop 11.1:
Exploring advanced privacy-preserving techniques (homomorphic encryption, zero-knowledge proofs).
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Workshop 11.2:
Utilizing homomorphic encryption for secure computation.
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Workshop 11.3:
Implementing zero-knowledge proofs for data verification.
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Workshop 11.4:
Designing and building advanced privacy-preserving systems.
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Workshop 11.5:
Optimizing advanced techniques for specific applications.
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Workshop 11.6:
Best practices for advanced techniques.
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Workshop 12.1:
Implementing privacy-preserving ML for medical data analysis.
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Workshop 12.2:
Utilizing privacy-preserving ML for financial fraud detection.
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Workshop 12.3:
Implementing privacy-preserving ML for social network analysis.
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Workshop 12.4:
Utilizing privacy-preserving ML for personalized recommendations.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing TensorFlow Privacy, PySyft, and OpenMined.
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Workshop 13.2:
Implementing privacy-preserving models with frameworks.
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Workshop 13.3:
Designing and building privacy-preserving pipelines.
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Workshop 13.4:
Optimizing tool usage for efficient development.
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Workshop 13.5:
Best practices for framework implementation.
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Workshop 14.1:
Implementing model evaluation and privacy auditing.
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Workshop 14.2:
Utilizing privacy metrics and audit tools.
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Workshop 14.3:
Designing and building monitoring systems for privacy risks.
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Workshop 14.4:
Optimizing evaluation for privacy compliance.
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Workshop 14.5:
Best practices for evaluation.
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Workshop 15.1:
Emerging trends in privacy-preserving machine learning.
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Workshop 15.2:
Utilizing AI for privacy-enhancing technologies.
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Workshop 15.3:
Implementing privacy-preserving ML in edge computing environments.
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Workshop 15.4:
Best practices for future applications.
