Anonymising Data using k-Anonymity

Data anonymisation is an important approach in data governance and management; it enables organisations to protect personal information while sharing, processing, and making use of such information. However, anonymisation still carries the risk of re-identification.  A well-known approach which allows organisations to quantify the risk of re-identification utilises k-anonymity.

This hands-on, interactive course provides an in-depth exploration of k-Anonymity. An anonymisation process can use various techniques to achieve the k-threshold; several of these will be explored in theory and exercises in terms of how they contribute towards k-Anonymity. The course introduces the theoretical foundations, and it complements the understanding with exercises and discussions. It also shows how to apply the 5-step process promoted by Singapore’s Personal Data Protection Commission (PDPC) in its latest Anonymisation Guide (published early 2022). The exercises will use the Academy’s own learning tool.

The course will also cover legal aspects of anonymisation as far as relevant for the understanding and practical use of k-Anonymity. It will further address risk considerations for assessing the sufficiency of anonymisation. Though more technical in nature, this course is suitable for managers and directors to get a better understanding of anonymisation, and how to address and manage re-identification and other threats as part of their risk management responsibilities.

Who should attend?

• Privacy Engineers / Technical staff / Developers / Data Analysts / Project Managers / Data Architects
• Data Protection Officers (DPOs) / Compliance Professionals / In-house counsels
• Executives, and staff involved in the use, management, and protection of an organisation’s information and data

Course Details

To view available dates and register for this course, please click here. You may also register for this course and view all available courses on our course schedule page (www.drewnapier.com/Academy/Course-Schedule).

Course Outline

Overview of anonymisation (Know the rules and your data)

• Data anonymisation:
• Anonymity and data anonymisation
• Data attributes
• Keep data secret versus keep identity secret versus protect new insights

• Basic legal aspects under Personal Data Protection Act (PDPA):
• Advisory Guidelines and PDPC Anonymisation Guide
• Use cases and scenarios involving anonymisation

• Identification, Identifiability, and sensitivity of data:
• Anonymisation scale
• PDPC Guide’s release models

Basic anonymisation (Know the process, techniques, and relationships)

• PDPC’s 5 step process
• Key techniques and exercises
• Utility versus information loss: Basics of information content and information entropy

Details of k-anonymity with exercises (Know k-anonymity measure)

• Origin and intent of k-anonymity
• Concept of equivalence class
• K-anonymity: Protection against re-identification and limitations
• Exercises on small and complex data sets

Course Director & Facilitator

Albert Pichlmaier   Senior Cybersecurity and Privacy Engineer Senior Learning Technology Designer, Drew Data Protection & Cybersecurity Academy