Certified AI Crypto & Privacy Engineer (CAICPE)

The CAICPE Certification Program equips professionals with the skills to design and implement cryptographic and privacy-preserving techniques in AI systems. Participants will explore secure learning paradigms, encryption methods, differential privacy, and trusted environments. The course emphasizes privacy engineering principles and responsible AI data practices, enabling learners to address real-world AI privacy challenges across industries. With a focus on practical frameworks and risk mitigation strategies, this program prepares engineers to balance performance, usability, and confidentiality in AI systems while staying compliant with evolving regulations.

Audience:

• AI and ML engineers
• Cybersecurity professionals
• Privacy engineers
• Data scientists
• System architects
• Compliance and risk officers

Learning Objectives:

• Understand core cryptographic techniques used in AI
• Apply differential privacy to AI model training and inference
• Design privacy-aware systems using secure multiparty computation
• Leverage trusted execution environments for secure AI
• Manage privacy budgets and regulatory compliance in AI

Program Modules:

Module 1: Foundations of AI Privacy and Cryptography

• Principles of privacy engineering in AI
• Overview of applied cryptography for AI systems
• Privacy risks in machine learning pipelines
• Regulatory implications: GDPR, HIPAA, and more
• Cryptographic primitives and their relevance to AI
• Threat modeling for privacy-preserving AI

Module 2: Federated Learning and Secure Aggregation

• Federated learning architecture and communication flow
• Secure aggregation protocols and their benefits
• Data minimization principles in federated learning
• Challenges in cross-device AI collaboration
• Threats in decentralized training environments
• Use cases: healthcare, finance, edge AI

Module 3: Homomorphic Encryption and SMPC

• Concepts of homomorphic encryption
• Use of partially and fully homomorphic schemes
• Secure multiparty computation for collaborative AI
• Limitations and performance trade-offs
• Integration into AI workflows
• Tools and libraries overview

Module 4: Differential Privacy Techniques

• Local vs. global differential privacy
• Privacy-preserving data collection techniques
• Noise injection strategies for model training
• Differential privacy in inference phase
• Measuring and tracking privacy loss
• Real-world applications and case studies

Module 5: Trusted Execution Environments (TEE)

• Overview of Intel SGX and AWS Nitro
• Confidential computing principles
• Enclave-based model execution
• Data confidentiality in untrusted infrastructures
• TEE implementation challenges
• TEE-based AI use cases

Module 6: Privacy Budgeting and Governance

• Concept of privacy budgets in AI
• Trade-offs between utility and privacy
• Tools for monitoring cumulative privacy loss
• Governance frameworks for responsible AI
• Privacy-preserving metrics and auditing
• Aligning with organizational compliance policies

Exam Domains:

1. Cryptography Essentials for AI
2. Secure Federated AI Systems
3. Privacy Techniques in AI Model Lifecycle
4. Trusted Execution and Secure Infrastructure
5. AI Privacy Risk Management
6. Governance, Ethics, and Compliance in AI

Course Delivery:

The course is delivered through a combination of lectures, interactive discussions, and project-based learning, facilitated by experts in the field of AI privacy and cryptography. 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 Certified AI Crypto & Privacy Engineer (CAICPE).

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 Crypto & Privacy Engineer (CAICPE) Certification Training exam, candidates must achieve a score of 70% or higher.

Advance your AI privacy expertise. Enroll in the CAICPE Certification Program by Tonex today and lead the future of secure and responsible AI.