Certified AI Data Privacy Engineer (CADPE) Certification Course by Tonex
This certification focuses on how AI intersects with data privacy, ensuring that AI models handle personal data securely and in compliance with global privacy regulations such as GDPR, CCPA, and HIPAA.
Learning Objectives:
- Understand how AI intersects with data privacy and the implications for handling personal data.
- Comprehend global privacy regulations such as GDPR, CCPA, and HIPAA, and their impact on AI systems.
- Apply Privacy-By-Design principles in AI model development to ensure data security and privacy.
- Ensure AI models comply with data privacy laws and standards.
- Implement data governance, security measures, and risk management for AI systems.
- Utilize data minimization and anonymization techniques to protect personal data in AI.
- Address ethical, legal, and compliance challenges in AI systems related to data privacy.
- Evaluate and mitigate risks in AI systems concerning personal data handling.
- Ensure transparency, accountability, and explainability in AI models processing personal data.
- Stay updated on emerging trends and innovations that enhance privacy in AI systems.
Target Audience: Data scientists, cybersecurity professionals, privacy officers, AI/ML engineers.
Program Modules:
Module 1: Ensuring Data Privacy in AI Training Datasets
- Identifying personal data in AI training datasets.
- Techniques for reducing sensitive data exposure during model training.
- Strategies for ensuring data minimization in training datasets.
- Managing data retention and deletion policies.
- Auditing datasets for privacy vulnerabilities.
- Tools for detecting privacy violations in AI datasets.
Module 2: Implementing Differential Privacy in AI Models
- Understanding the concept of differential privacy.
- Applying differential privacy mechanisms in AI models.
- Balancing accuracy and privacy in AI systems.
- Techniques for adding noise to datasets without compromising model performance.
- Evaluating privacy guarantees in differential privacy frameworks.
- Case studies on differential privacy implementation in AI.
Module 3: Secure Data-Sharing Techniques for AI Systems
- Overview of secure multi-party computation in AI.
- Data encryption methods for sharing across AI systems.
- Homomorphic encryption and its use in AI data-sharing.
- Federated learning for decentralized AI model training.
- Managing access controls and permissions in AI data sharing.
- Ensuring secure data exchange across cloud-based AI systems.
Module 4: Compliance with GDPR, CCPA, and Other Privacy Frameworks
- Overview of GDPR, CCPA, and other major privacy regulations.
- Key privacy principles in AI under global regulations.
- Ensuring lawful basis for data processing in AI.
- Implementing data subject rights in AI models (e.g., right to access, right to be forgotten).
- Developing policies and practices for regulatory compliance.
- Privacy impact assessments and audits for AI systems.
Module 5: Techniques for Anonymization and Pseudonymization in AI
- Understanding the difference between anonymization and pseudonymization.
- Techniques for data anonymization in AI.
- Pseudonymization methods to protect identifiable data.
- Trade-offs between utility and privacy in data anonymization.
- Re-identification risks and how to mitigate them.
- Best practices for maintaining privacy in AI outputs.
Module 6: Risk Management and Auditing for AI Privacy Compliance
- Identifying and assessing privacy risks in AI systems.
- Tools for conducting AI privacy impact assessments (PIAs).
- Implementing ongoing privacy monitoring and auditing of AI models.
- Responding to data breaches and privacy incidents in AI.
- Ensuring accountability and transparency in AI data processing.
- Building an AI privacy compliance framework within organizations.
Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, hands-on workshops, and project-based learning, facilitated by experts in the field of AI data privacy. Participants will have access to online resources, including readings, case studies, and tools for practical exercises.
Assessment and Certification:
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in AI data privacy.
Exam domains:
- AI and Data Privacy Fundamentals – 15%
- Global Privacy Regulations (GDPR, CCPA, HIPAA, etc.) – 20%
- Privacy-By-Design and Data Minimization in AI – 15%
- Differential Privacy and Anonymization Techniques – 15%
- Secure Data-Sharing and Governance in AI Systems – 15%
- Risk Management, Auditing, and Compliance in AI – 20%
Question Types:
- Multiple Choice Questions (MCQs)
- True/False Statements
- Scenario-based Questions
- Fill in the Blank Questions
- Matching Questions (Matching concepts or terms with definitions)
- Short Answer Questions
Passing Criteria:
To pass the Certified AI Data Privacy Engineer (CADPE) Certification exam, candidates must achieve a score of 70% or higher.