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HBO/WO

MBO

August 23, 2023

Practical: Applying and evaluating privacy-preserving techniques

Gain practical skills to mitigate privacy leakages by applying Differential Privacy How to anonymize datasets with Differential Privacy How to train ML models with Differential Privacy Understanding the difference between data anonymization and privacy-preserving model training Study the trade-off between privacy preservation (anonymization) and utility (model quality, data accuracy)

Website

HCAIM Consortium

May 31, 2023

Data Privacy Handbook

The Data Privacy Handbook is a practical guide on handling personal data in scientific research, primarily written for Utrecht University researchers and research support staff in the Netherlands. It is an initiative of Research Data Management Support, in collaboration with privacy and data experts at Utrecht University. The Data Privacy Handbook consists of: A knowledge base which explains how the EU General Data Protection Regulation (GDPR, Dutch: Algemene Verordening Gegevensbescherming) applies to scientific research, including guidelines and good practices in carrying out GDPR-compliant scientific research; An overview of privacy-enhancing techniques & tools and practical guidance on their implementation; Use Cases in the form of research projects with privacy-related issues, for which a reusable solution (e.g., tool, workflow) is shared.

Website

Universiteitsbibliotheek Utrecht

August 23, 2023

Interactive session: Perspectives on privacy

Understand, analyze and elaborate upon the importance of AI privacy and risk management in the various application sectors. Discern, investigate and discuss key risks which AI and Machine Learning models introduce and how experts from specific fields and sectors have chosen to address them. How to create a governance framework in your organization to enable AI risk management Summarize and understand the working principles, privacy and risks which AI systems introduce and how to address them. Expand on possible applications of privacy and security controls at each phase of the Artificial Intelligence lifecycle, based on sector-specific applications of AI. Open the discussion towards Privacy-Preserving Machine Learning and Privacy-Preserving Techniques.

Website

HCAIM Consortium

August 23, 2023

Lecture: Introduction to privacy and risk

Obtain a general understanding of the notion of privacy. Understand the difficulties and pitfalls of data privacy analysis and detection of personal data. Understand the trade-off between anonymization and data utility (no free lunch). Understand the difference between data security and data privacy. Learn the basic principles of anonymous communication and TOR. Discern, investigate and discuss key risks which AI and Machine Learning models introduce

Website

HCAIM Consortium

May 19, 2023

Privacy Basics for Researchers: online module materials

These are the raw materials used in the online training module "Privacy Basics for Researchers", created within Utrecht University's Moodle platform, by Utrecht University's Research Data Management Support. This module was developed as part of the Data Privacy Project, to give Utrecht University researchers a basic understanding of the General Data Protection Regulation in a research context. It is a self-paced module that takes about 1.5 hours to complete. The module focuses on the following topics: Chapter 1: Personal data, sensitive personal data, roles in the General Data Protection Regulation (GDPR) Chapter 2: Legal basis in research, informing data subjects, data subjects' rights Chapter 3: Privacy by Design and Privacy by Default, privacy scan, Data Protection Impact Assessment (DPIA), privacy risks, data breach Chapter 4: Data security, access control, encryption, pseudonymisation, anonymisation, tools Chapter 5: Storing and sharing personal data, making personal data Findable, Accessible, Interoperable and Reusable (FAIR) In the deposit, you can find the following files: An overview of the module's blocks and learning goals (Learning-Goals_Privacy-Basics-for-Researchers.pdf) The module content in the form of a storyboard per chapter in the online platform Self-created images used in the platform (Platform_materials.zip). A raw Moodle export of the module that can be imported in any other Moodle platform (Privacy_Basics_for_Researchers_pbfr_20230512.mbz)

Document

Dorien Huijser, Ruud Dielen, Jacques Flores-Dourojeanni, Mercedes Beltrán

August 23, 2023

Practical: Auditing frameworks of privacy and data protection

Improve the practical skills of auditing the privacy (and confidentiality) guarantees of machine learning How to apply membership attacks and attribute inference attacks for ML privacy auditing

Website

HCAIM Consortium

August 23, 2023

Interactive Session: Privacy Preserving Machine Learning

Understand the concept of Privacy Preserving Fundamentals Discuss application of PPML in practical Scenarios

Website

HCAIM Consortium

August 23, 2023

Lecture: Privacy and machine learning

Understanding of privacy risks in machine learning Distinguish training data and model extractions attacks/threats Learn adversarial modelling and threat analysis in AI Learn the principles of AI privacy auditing Distinguish membership and reconstruction attacks Distinguish membership attack and model inversion

Website

HCAIM Consortium

August 23, 2023

Lecture: Privacy

Learner understands what the possible consequences of the large amounts of data that the large tech companies collect are for society. Learner understands what the difference between tech companies and governments that collect data is. Learner understands why the surveillance capitalism outlined by Zuboff is different from market capitalism as we knew it until recently. Learner understands ethical challenges regarding anonymization.

Website

HCAIM Consortium

October 13, 2026

E-learning Pijler 4.2 Online privacy

In deze e-learning leer je wat online privacy is en hoe jouw persoonsgegevens worden verzameld wanneer je websites, apps en sociale media gebruikt. Dit is de tweede e-learning (2/5) uit de reeks korte e-learnings bij DigComp Pijler 4, deze e-learning is voor competentie 4.2. De e-learning bestaat uit een introductie-animatie, uitleg over persoonsgegevens, vragen over situaties waar je bewust keuzes moet maken over hoe data wordt verzameld. Je krijgt ook uitleg over welke privacyrechten je hebt. Er is ook een samenvatting. Leerdoel 1: Je kunt uitleggen wat online privacy en persoonsgegevens zijn. Leerdoel 2: Je herkent hoe gegevens online worden verzameld en welke bewuste keuzes je kunt maken om je privacy beter te beschermen. Leerdoel 3: Je weet welke rechten je hebt als data onterecht wordt verzameld. Geschatte studietijd: 15 minuten.

Secondary vocational educationWebsite

Project Digitale Vaardigheden voor Studenten en Team Content Creation