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Quest Training

Data Anonymization, Pseudonymization & Privacy-Enhancing Technologies Training Course

Teaches data teams to anonymize and pseudonymize personal data, test re-identification risk and apply privacy-enhancing technologies to share data safely.

DPGP · Data Privacy, Governance & ProtectionAll Levels#3031ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Data, Analytics & Digital TransformationCategories:Data Privacy, Governance & Protection

Course Overview

This advanced practical course equips participants to de-identify personal data so that it can be analyzed, shared, and published safely. It explains the technical and legal difference between anonymization, where individuals can no longer be identified by any means reasonably likely to be used, and pseudonymization, where identifiers are replaced but the data remains personal data subject to laws such as the Saudi Personal Data Protection Law (PDPL). The program covers the full de-identification toolkit, from classifying direct identifiers and quasi-identifiers to generalization, suppression, masking, noise addition, tokenization, and keyed hashing. It also examines formal privacy models, including k-anonymity, l-diversity, t-closeness, and differential privacy, and how to choose parameters that protect individuals while preserving analytical value. Participants learn to test re-identification risk through linkage, singling-out, and inference scenarios, and to evaluate privacy-enhancing technologies such as synthetic data, secure multi-party computation, homomorphic encryption, federated learning, and secure data environments for analytics and research. Through hands-on exercises on realistic datasets, participants will design de-identification plans, measure residual risk and data utility, govern pseudonymization keys, and document defensible release decisions for internal analytics, data sharing, and open data, in line with guidance issued by the Saudi Data and Artificial Intelligence Authority (SDAIA) on anonymization and pseudonymization.

Learning Objectives

  • Distinguish anonymization, pseudonymization, and de-identification in technical and legal terms.
  • Analyze how data protection laws treat pseudonymized and anonymized data.
  • Classify direct identifiers, quasi-identifiers, and sensitive attributes in a dataset.
  • Apply generalization, suppression, masking, perturbation, and tokenization techniques correctly.
  • Design pseudonymization schemes with secure key management and separation of duties.
  • Apply k-anonymity, l-diversity, and t-closeness to structured datasets.
  • Explain differential privacy concepts, privacy budgets, and noise calibration.
  • Assess re-identification risk using linkage, singling-out, and inference tests.
  • Measure the trade-off between residual privacy risk and data utility.
  • Evaluate synthetic data and other privacy-enhancing technologies for specific use cases.
  • Design secure data environments and controlled access models for analytics.
  • Establish governance, documentation, and periodic review for de-identified data releases.

Who Should Attend

This course is designed for Data Engineers, Data Analysts, Data Scientists, Statisticians, Privacy Officers, Data Protection Officers, Data Governance Specialists, Open Data Managers, and Information Security Professionals who prepare, protect, or release datasets containing personal data. It is also suitable for teams in national statistics offices, government ministries, banks, insurers, telecommunications operators, energy and oil and gas companies, healthcare administration, and research institutions that share data with partners, regulators, researchers, or the public. The program is particularly relevant to organizations building analytics platforms, data-sharing agreements, or open data portals that need a repeatable and evidence-based method for making personal data safe to use.

Learning Outcomes

  • Explain when data is anonymized, pseudonymized, or still identifiable.
  • Identify direct identifiers, quasi-identifiers, and sensitive attributes in real datasets.
  • Select de-identification techniques appropriate to the data type and release context.
  • Implement pseudonymization with secure key management and controlled re-linking.
  • Apply k-anonymity, l-diversity, and t-closeness and interpret their results.
  • Conduct re-identification risk tests, including motivated intruder assessments.
  • Quantify information loss and confirm that de-identified data remains fit for purpose.
  • Explain differential privacy parameters and their effect on accuracy.
  • Evaluate synthetic data for fidelity, utility, and disclosure risk.
  • Recommend suitable privacy-enhancing technologies for analytics and data-sharing scenarios.
  • Design secure data environments with output checking and access controls.
  • Document and govern de-identified data releases with periodic risk review.

Course Outline

Day 1

De-identification Foundations & Legal Context

  • Why organizations de-identify data.
  • Anonymization, pseudonymization, and de-identification compared.
  • Pseudonymized and anonymized data under the Saudi PDPL.
  • SDAIA guidance on anonymization and pseudonymization.
  • Direct identifiers, quasi-identifiers, and sensitive attributes.
  • Profiling datasets and the release context.
  • Release models: internal use, controlled sharing, and open publication.
  • Roles of data owners, privacy officers, and data engineers.
  • Common de-identification failures.
  • Practical Application: Profile a sample dataset and classify its identifiers and attributes.
Day 2

Pseudonymization & Core Anonymization Techniques

  • Pseudonyms from counters, random tokens, and keyed hashing.
  • Why plain hashing of identifiers is not sufficient.
  • Tokenization, format-preserving encryption, and token vaults.
  • Key management, re-linking controls, and separation of duties.
  • Generalization, recoding, and top and bottom coding.
  • Record and cell suppression strategies.
  • Masking of free text, dates, and location data.
  • Perturbation: noise addition, data swapping, and microaggregation.
  • Longitudinal, transactional, and unstructured data.
  • Practical Application: Pseudonymize and anonymize a customer dataset with key governance.
Day 3

Privacy Models & Re-identification Risk Testing

  • Re-identification attacks: linkage, singling out, and inference.
  • Prosecutor, journalist, and marketer risk scenarios.
  • k-anonymity: equivalence classes and minimum group sizes.
  • l-diversity and t-closeness for sensitive attributes.
  • Statistical disclosure control for aggregate tables.
  • Motivated intruder testing and red-team exercises.
  • Measuring information loss and analytical utility.
  • Setting risk thresholds and acceptance criteria.
  • Documenting risk test results and residual risk.
  • Practical Application: Test re-identification risk, apply k-anonymity, and compare utility.
Day 4

Differential Privacy & Privacy-Enhancing Technologies

  • Principles of differential privacy and the privacy budget.
  • Noise mechanisms, sensitivity, and calibration.
  • Differentially private statistics, dashboards, and queries.
  • Synthetic data generation approaches and use cases.
  • Evaluating synthetic data fidelity, utility, and privacy.
  • Secure multi-party computation and homomorphic encryption.
  • Federated learning and trusted execution environments.
  • Membership inference risks in machine learning.
  • Matching technologies to use cases.
  • Practical Application: Select privacy-enhancing technologies for a cross-organization analytics case.
Day 5

Secure Data Environments, Open Data & Governance

  • The Five Safes framework for safe data access.
  • Designing secure data environments and controlled research access.
  • Output checking and disclosure review procedures.
  • Preparing de-identified datasets for open data portals.
  • Data-sharing agreements and re-identification prohibitions.
  • De-identification policies, standards, and approval workflows.
  • Release registers, documentation, and audit evidence.
  • Periodic re-assessment of released data.
  • Building organizational de-identification capability.
  • Final Workshop: Present an end-to-end de-identification and release plan covering techniques, risk testing, utility, access controls, and governance.

Upcoming Dates

Selected date

Barcelona · 16 – 20 Nov 2026 · Classroom

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8 – 12 Nov 2026€3,100—
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22 – 26 Nov 2026€4,000€3,600until 2 Nov
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6 – 10 Dec 2026€3,300€2,970until 16 Nov
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14 – 18 Dec 2026€4,300€3,870until 23 Nov
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