Privacy-Preserving AI (PP-AI) Essentials Training by Tonex
This course by Tonex focuses on state-of-the-art techniques and strategies for safeguarding data in AI applications. Learn how to design, implement, and manage AI systems that prioritize privacy and comply with regulations. This program is ideal for professionals aiming to balance innovation with data protection.
Learning Objectives:
- Understand key privacy-preserving AI technologies.
- Learn how to implement federated learning, differential privacy, and encryption.
- Ensure compliance with global data privacy regulations.
- Secure sensitive data in AI workflows.
- Design AI solutions with privacy-by-design principles.
- Mitigate risks in AI-driven decision-making processes.
Target Audience:
- Privacy officers
- Data scientists
- AI engineers
- Compliance managers
- IT security professionals
- Policy makers
Course Modules:
Module 1: Introduction to Privacy-Preserving AI
- Overview of PP-AI concepts
- Importance of data privacy in AI
- Ethical considerations in AI development
- Global data privacy regulations overview
- Challenges in AI-driven data protection
- Benefits of privacy-first AI systems
Module 2: Federated Learning
- Fundamentals of federated learning
- Designing distributed AI models
- Managing data silos securely
- Use cases in healthcare and finance
- Challenges in federated learning adoption
- Tools and frameworks for implementation
Module 3: Differential Privacy
- Principles of differential privacy
- Adding noise to datasets
- Measuring privacy guarantees
- Real-world applications
- Differential privacy in data sharing
- Tools and libraries
Module 4: Homomorphic Encryption
- Basics of homomorphic encryption
- Encryption methods for AI models
- Secure computation on encrypted data
- Performance considerations
- Applications in sensitive industries
- Tools for implementation
Module 5: Privacy-by-Design in AI Development
- Privacy-first system architecture
- Data minimization strategies
- Incorporating privacy into AI workflows
- Risk assessment and mitigation
- Integrating compliance into AI design
- Case studies in privacy-preserving AI
Module 6: Securing AI Pipelines
- Identifying vulnerabilities in AI systems
- Secure data storage and transmission
- AI model attack prevention
- Role of encryption in securing pipelines
- Monitoring and auditing for compliance
- Building resilient AI systems
Join Tonex’s Privacy-Preserving AI training to master cutting-edge tools and techniques for safeguarding sensitive data in AI systems. Elevate your expertise and lead the way in secure AI innovation. Register now!