Introduction

In an age dominated by data-driven decision-making, Privacy Enhancing Technologies (PETs) have emerged as critical tools for safeguarding personal data and ensuring compliance with global data privacy regulations. Training Course on Privacy Enhancing Technologies (PETs) offers an in-depth understanding of modern PETs, including homomorphic encryption, differential privacy, federated learning, secure multiparty computation, and zero-knowledge proofs. Designed for data professionals, policymakers, and tech leaders, this course equips learners with practical tools and real-world strategies to implement PETs across various sectors.

This course responds to increasing demand for data privacy, cybersecurity compliance, and GDPR readiness across industries. By integrating case studies and interactive exercises, learners will gain actionable insights to minimize data exposure risks while maintaining operational efficiency. Participants will explore cutting-edge privacy solutions that enhance data utility without compromising confidentiality, aligning with industry best practices and legal frameworks.

Programme Curriculum

Training Course on Privacy Enhancing Technologies (PETs)

Introduction

Course Objectives

Understand the core principles of Privacy Enhancing Technologies (PETs).
Explore the regulatory landscape including GDPR, HIPAA, and CCPA.
Identify the risk factors of personal data exposure.
Learn how differential privacy ensures secure data analysis.
Apply homomorphic encryption for secure computations on encrypted data.
Demonstrate the use of federated learning in decentralized AI applications.
Evaluate the role of secure multiparty computation (SMPC) in collaborative environments.
Understand the application of zero-knowledge proofs in privacy authentication.
Design a privacy-first architecture for digital solutions.
Assess privacy threats in AI and machine learning models.
Implement privacy-by-design principles in software development.
Utilize PETs in financial and healthcare data sharing.
Create a roadmap for enterprise PETs adoption.

Target Audience

Data Scientists & AI Developers
Information Security Analysts
Privacy Officers & Compliance Managers
IT Architects & System Engineers
Government Policy Makers
Healthcare Data Managers
Financial Risk Officers
Legal Advisors in Data Law

Course Duration: 5 days

Course Modules

Module 1: Introduction to Privacy Enhancing Technologies

Overview of PETs and their significance
Key categories of PETs and use cases
Legal and ethical frameworks
PETs in today's digital economy
Common misconceptions about PETs
Case Study: Facebook's use of PETs in ad targeting

Module 2: Differential Privacy and Data Anonymization

Introduction to differential privacy
Techniques for data anonymization
Noise injection and utility tradeoffs
Tools like Google’s RAPPOR and Apple’s approach
Use in large-scale data analytics
Case Study: U.S. Census Bureau’s use of differential privacy

Module 3: Homomorphic Encryption and Secure Computation

What is homomorphic encryption (HE)?
Applications in finance and health sectors
Full vs. partial HE techniques
HE performance and scalability challenges
Popular libraries and implementation frameworks
Case Study: Encrypted disease outbreak prediction in healthcare

Module 4: Federated Learning and Decentralized Data Use

Fundamentals of federated learning
Benefits in mobile and edge computing
Challenges with model convergence
Privacy vs. accuracy dilemma
Real-world applications in healthcare and banking
Case Study: Google Gboard's on-device federated learning

Module 5: Secure Multiparty Computation (SMPC)

How SMPC works
Applications in privacy-preserving voting and finance
Data collaboration across organizations
Threat models and mitigation strategies
Limitations and computation overhead
Case Study: Private set intersection in credit risk analysis

Module 6: Zero-Knowledge Proofs (ZKPs)

Intro to cryptographic zero-knowledge proofs
How ZKPs enable secure authentication
ZKPs in blockchain and Web3
zk-SNARKs vs. zk-STARKs
Efficiency and practical implementations
Case Study: Zcash cryptocurrency and private transactions

Module 7: Privacy in AI and Machine Learning

Privacy attacks on ML models (inference, extraction)
PETs for model training and inference
Synthetic data generation
Regulatory and ethical considerations
Best practices for AI data privacy
Case Study: Federated learning in COVID-19 diagnosis modeling

Module 8: Implementing PETs in Enterprise Systems

Developing a PETs integration strategy
Compliance and risk management alignment
Vendor solutions vs. in-house PETs development
Training and governance framework
Cost-benefit analysis and ROI
Case Study: PET adoption roadmap in a multinational bank

Training Methodology

Interactive presentations using real-world PET applications
Hands-on labs with popular PET tools and simulators
Group activities and breakout sessions for collaborative learning
Case study discussions tied to each module
Pre-assessments and post-assessments to measure learning outcomes
Access to supplementary materials and toolkits for practical implementation

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Privacy Tech Data Security Compliance IT

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000