Dr. Gergely Ács

Assistant Professor
e-mail: acs (at) crysys.hu

web: www.crysys.hu/~acs/
office: I.E. 430
tel: +36 1 463 2047
fax: +36 1 463 3263

Short Bio

Gergely ÁCS [gergej ɑ:tʃ] received the M.Sc. and Ph.D. degree in Computer Science from the Budapest University of Technology and Economics (BME), where he conducted research in the Laboratory of Cryptography and System Security (CrySyS). Currently, he is an assistant professor at Budapest University of Technology and Economics (BME), in Hungary. Before that, he was a post-doc and then research engineer in Privatics Team at INRIA, in France. His general research interests include data privacy and security.

Current Courses

IT Security (VIHIAC01)

This BSc course gives an overview of the different areas of IT security with the aim of increasing the security awareness of computer science students and shaping their attitude towards designing and using secure computing systems. The course prepares BSc students for security challenges that they may encounter during their professional carrier, and at the same time, it provides a basis for those students who want to continue their studies at MSc level (taking, for instance, our IT Security minor specialization). We put special emphasis on software security and the practical aspects of developing secure programs.

IT Security (in English) (VIHIAC01)

This BSc course gives an overview of the different areas of IT security with the aim of increasing the security awareness of computer science students and shaping their attitude towards designing and using secure computing systems. The course prepares BSc students for security challenges that they may encounter during their professional carrier, and at the same time, it provides a basis for those students who want to continue their studies at MSc level (taking, for instance, our IT Security minor specialization). We put special emphasis on software security and the practical aspects of developing secure programs.

Student Project Proposals

Image-based profiling

Images/photos of users is one of the highly-available personal on-line information. People often publish images on different social media (e.g., Facebook or Instagram) without disclosing their private attributes such as their interests, list of friends, or places that they visited. Although it is usually fairly easy for humans to infer private information (e.g., age, sex, interests, locations, etc.) about the image owner by carefully analyzing different objects of the image (faces, background objects, activities, etc.) [1], the automation of such privacy attacks had been difficult before the advent of Deep Learning.

Inference of sensitive data from location information

Location information (e.g., GPS trajectories) is one of the most useful data that companies wish to monetize by sharing it with other entities with the appropriate expertise to analyze it. It is not hard to see that location information is very sensitive information. The list of visited places can reveal, among other things, the religion/political beliefs or information about the health life. While such information is often easy to infer (e.g., did the person stop near a hospital?), it can be less apparent in other cases due to subtle data correlation. For example, when the New York City Taxi and Limousine Commission published a dataset of every yellow cab ride in New York in 2013, muslim taxi drivers were easy to identify as they stopped 5 times per day for more than 20 minutes to pray.