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.
ANNOUNCEMENT: MOVING TO ONLINE TEACHING
Teaching of the course Privacy-preserving Technologies (VIHIAV35) will be continued using on-line methods from the week of September 9, 2020 as follows:
We will pre-record the lectures and make the recording available in video form. The slides and additional resources (papers) in PDF format will be available in the Moodle system of the department as before. In addition, there will be an URL pointing to the video of the lecture. The video will be made available via YouTube. Youtube access will require you to be online obviously.
At the regular scheduled time intervals of the lecture, we will hold an on-line consultation using MS Teams. Those who want to participate at this consultation should get familiar with the course material (see above) before the consultation, and use the consultation to ask questions. Questions can also be sent to the lecturers before the consultation by sending an e-mail to firstname.lastname@example.org. The lecturer will prepare for answering the received questions and begin the consultation with discussing those questions. Then he will respond to any further questions received during the consultation via the chat panel of the Teams meeting. The consultations will be held in the second 45 mins of the scheduled time interval of the course, i.e., 1.15pm-2pm every Wednesday. The first 45 mins can be used to study the parts of the video lecture you have not watched before: keep in mind that video lectures will vary in duration in the 50-90 minute range. It is NOT mandatory to participate in these consultations.
The test will be conducted on Moodle at the scheduled time (9th December, 2020). It will a mix of quiz and essay questions with time restrictions, and we will grade it manually. Naturally, to complete the test, you should not come to the university, but you will complete the test from home. More information on the test will be distributed in due time. It is clear that we will not be able to detect cheating perfectly (a set of technical measures will be in place of course), so I remind you that ethics in engineering is supremely important. Cheating is unfair to those who do not cheat and morally unacceptable; cheating should be avoided, even if it might be a Nash equilibrium :)
We will continue to use broadcast Neptun messages as an official form of communication with you, however we advise you to watch the VIHIAV35 Moodle and also this website. There will surely be glitches, we ask you to be patient and cooperative. We give our all to ensure that you keep receiving quality education.
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.
|2020.09.09||Introduction and Motivation||G. Acs|
|2020.09.16||Legal background of Data Protection: GDPR||G. Acs|
|2020.09.23||Cancelled (Do some sport)|
|2020.09.30||Cryptography for Privacy 1: Crypto Basics, Private Set Intersection, Homomorphic Encryption||M. Horváth|
|2020.10.07||Cryptography for Privacy 2: Secure Multiparty Computation, Oblivious Transfer, Private Information Retrieval||M. Horváth, G. Acs|
|2020.10.14||Privacy-preserving communication: Secure Messaging (Signal) and TOR||G. Acs|
|2020.10.21||Web Tracking and Anti-Tracking||G. Acs|
|2020.10.28||Personal data leakage from relational data: Uniqueness, Attribute Inference, Linking||Sz. Lestyán|
|2020.11.04||Personal data leakage from unstructured data: Detection with Machine Learning, Web page fingerprinting, Code stylometry||G. Acs|
|2020.11.11||Personal data leakage from aggregate data: Query auditing, Location recovery from density, Membership attack||G. Acs|
|2020.11.18||Data anonymization: K-anonymity, Differential Privacy, RAPPOR||Sz. Lestyán|
|2020.11.25||Privacy in Machine Learning: Modell inversion, Membership attack, Fairness||B. Pejó|
|2020.12.02||Psychological profiling and manipulation, Cognitive Security||G. Acs|
|2020.12.09||Mid-term test (ZH)||G. Acs|