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, encrypted traffic, source/binary code, 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 and the lectures such as slides, administrative information, and supplementary materials. Consequently, this page always under construction.



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





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



  • Thursday, 13:15-14:00', IE.220


Megbeszélés szerint, az előadóval előre egyeztetett időpontban.

Office hours

Please contact the lecturer to schedule an appointment.



Date Topic Lecturer
2021.09.09 Introduction and Motivation G. Acs
2021.09.16 Legal background of Data Protection: GDPR G. Acs
2021.09.23 Cryptography for Privacy 1: Crypto Basics, Private Set Intersection, Homomorphic Encryption M. Horváth
2021.10.30 Cryptography for Privacy 2: Secure Multiparty Computation, Oblivious Transfer, Private Information Retrieval M. Horváth, G. Acs
2021.10.07 Cancelled (Sch QPA)
2021.10.14 Privacy-preserving communication: Secure Messaging (Signal) and TOR G. Acs
2021.10.21 Web Tracking and Anti-Tracking G. Acs
2021.10.28 Personal data leakage from relational data: Uniqueness, Attribute Inference, Linking G. Acs
2021.11.04 Personal data leakage from unstructured data: Detection with Machine Learning, Web page fingerprinting, Code stylometry G. Acs
2021.11.11 Personal data leakage from aggregate data: Query auditing, Location recovery from density, Membership attack G. Acs
2021.11.18 Data anonymization: K-anonymity, Differential Privacy, RAPPOR G. Acs
2021.11.25 Privacy in Machine Learning: Modell inversion, Membership attack, Fairness B. Pejó
2021.12.02 Interdependent Privacy G. Biczok
2021.12.09 Mid-term test (ZH) G. Acs