Privacy-Preserving Technologies / Személyes adatok védelme (VIHIAV35)

This course provides an introduction into the practical problems of data protection and privacy. Students can develop skills of understanding and assessing privacy threats and designing countermeasures. The course focuses on the problem of unwanted personal and sensitive data leakage from different information sources (e.g., large datasets, web-tracking, machine learning models), and its detection as well as mitigations using Privacy Enhancing Technologies (PETS). Rules and requirements are also available in Hungarian on the official site of the course. This page is the course homepage, which contains practical information related to the course such as administrative information and schedule. Lecture slides and supplementary materials are available on Moodle.

Célkitűzés

Objectives

The aim is to deliver (mainly technical) knowledge required by the General European Data Protection Regulation (GDPR) from Data Protection Officers (DPOs).

Lecturers

Oktatók

Követelmények

Requirements

during the semester

1 mid-term test on the last lecture. The final grade is the grade obtained for the test. Failed classroom tests can be retaken again on the supplement week.

Órák ideje és helye

Time and location of classes

Előadás

Lecture

  • TBA, TBA

Konzultáció

Megbeszélés szerint, az előadóval előre egyeztetett időpontban.

Office hours

Please contact the lecturer to schedule an appointment.

Beosztás

Schedule

Date Topic Lecturer
TBA Introduction and Motivation B. Pejo
TBA Dark Patterns: Types, Countermeasures, and Cognitive Biases B. Pejo
TBA Tracking: Profiling, Data Brokers, and Web Tracking B. Pejo
TBA Legal background of Data Protection: GDPR B. Pejo
TBA Cryptography: Theory (HE/SMPC/OT/SS/PSI/PIR/ZKP) B. Pejo
TBA Cryptography: Applications (Cryptocurrencies/Tor/E-Voting B. Pejo
TBA Machine Learning Privacy: Model Extraction & Inversion, Membership & Property Inference, Reconstruction Attack, and Fairness B. Pejo
TBA Deanonymization: Relational data B. Pejo
TBA Deanonymization: Unstructured data B. Pejo
TBA Deanonymization: Aggregate data B. Pejo
TBA Anonymization: K-Anonimity B. Pejo
TBA Anonymization: Differential Privacy B. Pejó
TBA Guest Lecture
2022.12.08 Final test (ZH) B. Pejo