Privacy-preserving Machine Learning Training Course: Data Privacy Techniques

Introduction

Safeguard sensitive data with our Privacy-Preserving Machine Learning Training Course. This program is designed to equip you with the essential skills to protect data privacy in machine learning, enabling you to develop and deploy AI models that respect user confidentiality. In today's data-sensitive world, mastering privacy-preserving techniques is crucial for building trust and ensuring regulatory compliance. Our privacy-preserving machine learning training course offers hands-on experience and expert guidance, empowering you to integrate advanced privacy measures into your machine learning workflows.

This data privacy techniques training delves into the core concepts of privacy-preserving machine learning, covering topics such as differential privacy, federated learning, and secure multi-party computation. You'll gain expertise in using industry-standard tools and techniques to protect data privacy in machine learning, meeting the demands of modern data-driven organizations. Whether you're a machine learning engineer, data scientist, or privacy specialist, this Privacy-Preserving Machine Learning course will empower you to build and deploy robust, privacy-centric AI solutions.

Target Audience:

• Machine Learning Engineers
• Data Scientists
• Privacy Specialists
• Security Engineers
• Data Analysts
• Software Developers
• Anyone needing privacy-preserving ML skills

Course 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.

Duration

10 Days

Course content

Module 1: Introduction to Privacy-Preserving Machine Learning

• Fundamentals of privacy-preserving machine learning.
• Overview of differential privacy, federated learning, and secure multi-party computation.
• Setting up a privacy-preserving ML development environment.
• Introduction to privacy-preserving ML frameworks and tools.
• Best practices for privacy-preserving ML.

Module 2: Differential Privacy Techniques

• Implementing differential privacy techniques for data anonymization.
• Utilizing epsilon and delta parameters for privacy guarantees.
• Designing and building differentially private algorithms.
• Optimizing differential privacy for accuracy and utility.
• Best practices for differential privacy.

Module 3: Federated Learning for Decentralized Training

• Implementing federated learning for decentralized model training.
• Utilizing secure aggregation and model averaging.
• Designing and building federated learning systems.
• Optimizing federated learning for communication efficiency.
• Best practices for federated learning.

Module 4: Secure Multi-Party Computation (SMPC)

• Implementing secure multi-party computation for collaborative analysis.
• Utilizing secret sharing and homomorphic encryption.
• Designing and building secure computation protocols.
• Optimizing SMPC for performance and security.
• Best practices for SMPC.

Module 5: Privacy-Preserving Machine Learning Models

• Designing and building privacy-preserving machine learning models.
• Implementing privacy-preserving neural networks and decision trees.
• Utilizing privacy-preserving data augmentation techniques.
• Optimizing models for privacy and accuracy.
• Best practices for privacy-preserving models.

Module 6: Workflow Optimization for Data Confidentiality

• Optimizing machine learning workflows for data confidentiality.
• Utilizing data anonymization and pseudonymization.
• Implementing data access control and audit trails.
• Designing secure data processing pipelines.
• Best practices for workflow optimization.

Module 7: Troubleshooting Privacy Challenges

• Debugging common privacy challenges in machine learning.
• Analyzing privacy risks and vulnerabilities.
• Utilizing troubleshooting techniques for problem resolution.
• Resolving common privacy issues.
• Best practices for troubleshooting.

Module 8: Data Anonymization and De-identification

• Implementing data anonymization and de-identification techniques.
• Utilizing k-anonymity and l-diversity.
• Designing and building anonymization pipelines.
• Optimizing anonymization for data utility.
• Best practices for anonymization.

Module 9: Integration with Real-World Applications

• Integrating privacy-preserving ML with real-world applications.
• Utilizing APIs and data connectors.
• Implementing privacy-preserving ML in healthcare and finance.
• Optimizing integration for specific application domains.
• Best practices for integration.

Module 10: Sensitive Data and Data Governance

• Understanding how to handle sensitive data and ensure data governance.
• Utilizing data encryption and access control.
• Designing and building data governance policies.
• Optimizing data handling for regulatory compliance.
• Best practices for data governance.

Module 11: Advanced Privacy-Preserving Techniques

• Exploring advanced privacy-preserving techniques (homomorphic encryption, zero-knowledge proofs).
• Utilizing homomorphic encryption for secure computation.
• Implementing zero-knowledge proofs for data verification.
• Designing and building advanced privacy-preserving systems.
• Optimizing advanced techniques for specific applications.
• Best practices for advanced techniques.

Module 12: Real-World Use Cases

• Implementing privacy-preserving ML for medical data analysis.
• Utilizing privacy-preserving ML for financial fraud detection.
• Implementing privacy-preserving ML for social network analysis.
• Utilizing privacy-preserving ML for personalized recommendations.
• Best practices for real-world applications.

Module 13: Privacy-Preserving ML Frameworks and Tools Implementation

• Utilizing TensorFlow Privacy, PySyft, and OpenMined.
• Implementing privacy-preserving models with frameworks.
• Designing and building privacy-preserving pipelines.
• Optimizing tool usage for efficient development.
• Best practices for framework implementation.

Module 14: Model Evaluation and Privacy Auditing

• Implementing model evaluation and privacy auditing.
• Utilizing privacy metrics and audit tools.
• Designing and building monitoring systems for privacy risks.
• Optimizing evaluation for privacy compliance.
• Best practices for evaluation.

Module 15: Future Trends in Privacy-Preserving ML

• Emerging trends in privacy-preserving machine learning.
• Utilizing AI for privacy-enhancing technologies.
• Implementing privacy-preserving ML in edge computing environments.
• Best practices for future applications.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.