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 such as administrative information and schedule.
Lecture slides and supplementary materials are available on Moodle.
The aim is to deliver (mainly technical) knowledge required by the General European Data Protection Regulation (GDPR) from Data Protection Officers (DPOs).
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.
Megbeszélés szerint, az előadóval előre egyeztetett időpontban.
Please contact the lecturer to schedule an appointment.
|2022.09.08||Introduction and Motivation||G. Acs|
|2022.09.15||Legal background of Data Protection: GDPR||G. Acs|
|2022.09.22||Cryptography for Privacy 1: Crypto Basics, Private Set Intersection, Homomorphic Encryption||M. Horváth|
|2022.09.29||Cryptography for Privacy 2: Secure Multiparty Computation, Oblivious Transfer, Private Information Retrieval||M. Horváth, G. Acs|
|2022.10.06||Privacy-preserving communication: Secure Messaging (Signal) and TOR||G. Acs|
|2022.10.13||Web Tracking and Anti-Tracking||G. Acs|
|2022.10.20||Personal data leakage from relational data: Uniqueness, Attribute Inference, Linking||G. Acs|
|2022.10.27||Personal data leakage from unstructured data: Detection with Machine Learning, Web page fingerprinting, Code stylometry||G. Acs|
|2022.11.03||Personal data leakage from aggregate data: Query auditing, Location recovery from density, Membership attack||G. Acs|
|2022.11.10||Data anonymization: K-anonymity, Differential Privacy, RAPPOR||G. Acs|
|2022.11.17||Cancelled (Scientific Student Conference)|
|2022.11.24||Privacy in Machine Learning: Modell inversion, Membership attack, Fairness||B. Pejó|
|2022.12.01||Interdependent Privacy||G. Biczok|
|2022.12.08||Final test (ZH)||G. Acs|