Synthetic Data and Privacy-Preserving AI

Course Category : Data Management

An advanced programme exploring synthetic data and privacy-preserving techniques for enabling trustworthy AI while reducing disclosure, re-identification, and sensitive-data exposure risks.
Duration: 5 Days
Level: Advanced

Introduction

The growing dependence of AI systems on large-scale datasets has made privacy preservation a strategic requirement rather than a peripheral technical consideration. Organisations increasingly need approaches that retain analytical value while reducing unnecessary exposure of confidential, personal, or sensitive information.
This course examines synthetic data generation and evaluation alongside differential privacy, federated learning, and privacy-enhancing computation. It addresses re-identification and information leakage risks, data utility assessment, governance controls, and the integration of privacy considerations throughout the AI lifecycle. Participants develop a structured understanding of how privacy-preserving approaches can support trustworthy data use while maintaining appropriate organisational accountability..

Targeted Audience

Artificial Intelligence and Machine Learning Professionals
Data Scientists and Data Analysts
Data Governance and Data Management Professionals
Privacy and Data Protection Officers
Technology Risk and Compliance Professionals
AI and Digital Transformation Managers
Data Engineers and Architects
AI and Data Governance Consultants

Targeted Skills

Understanding Synthetic Data Architectures and Use Cases
Analysing Privacy-Preserving AI Techniques
Assessing Disclosure and Re-identification Risks
Understanding Differential Privacy Mechanisms
Understanding Federated Learning Models
Evaluating Synthetic Data Quality and Utility
Designing Governance Controls for Sensitive Data
Integrating Privacy into the AI Lifecycle

Expected Outcomes

Explain the fundamental principles of synthetic data and its relationship to AI development.
Differentiate among privacy-preserving approaches according to data characteristics and risk profiles.
Analyse data leakage and re-identification risks within AI environments.
Evaluate synthetic datasets in terms of quality, utility, and privacy.
Explain how differential privacy and federated learning can reduce sensitive-data exposure.
Identify governance controls required for synthetic data and privacy-preserving AI.
Integrate privacy and accountability considerations throughout the AI lifecycle.

Training Topics Index

Synthetic data concepts, characteristics, and sources
Differences between real, anonymised, and synthetic data
Organisational drivers for synthetic data adoption
Approaches to generating tabular, textual, and visual synthetic data
Opportunities, limitations, and risks associated with synthetic datasets

Privacy and data protection principles in AI environments
Information leakage risks from datasets and models
Re-identification and sensitive-information inference risks
Data minimisation, purpose limitation, and access controls
Privacy risk assessment across the data lifecycle

Differential privacy principles and protection mechanisms
Federated learning and decentralised sensitive-data processing
Encryption during computation and secure-computing concepts
Secure multiparty computation and protected collaboration
Selecting privacy-preserving techniques according to requirements and risks

Statistical fidelity and distributional similarity assessment
Measuring data utility for analytics and modelling
Managing the privacy–utility trade-off
Disclosure and re-identification risk indicators
Validation, documentation, and synthetic data quality assurance

Synthetic data governance, ownership, and accountability policies
Privacy-by-design across the AI lifecycle
Documentation of data sources, generation methods, and limitations
Oversight, accountability, and continuous risk management
Establishing an organisational framework for privacy-preserving AI

Course Features

Updated and Interactive Content
Hypothetical Examples and Case Studies
Pre- and Post-assessments to Measure Impact
Verified Certificate with a QR Verification Code

Synthetic Data and Privacy-Preserving AI