Institute of Informatics
Topic
Machine learning and sensitive data protection
PhD. program
Informatics / Applied Informatics
Name of the supervisor
doc. Ing. Giang NGUYEN, PhD.
Contact:
Receiving school
Faculty of Mathematics, Physics and Informatics, Comenius Uni in Bratislava
Annotation
Receiving schools (another possibilities):
- Faculty of Informatics and Information Technologies of STU in Bratislava (program: Applied Informatics)
- Faculty of Electrical Engineering and Informatics, Technical Uni of Košice (FEI TUKE, program: Informatics)
Many modern-world problems, which we want to solve with the help of AI, require direct access to sensitive data such as personal, medical, business, or security information. In many cases, getting access to such information is nearly impossible due to the protection requirements. From this point, several questions are raised, such as how to avoid sensitive information disclosure in the data science process or moreover, how can we model sensitive data without direct access to them. The topic of the work is focused on collaborative (federated) learning, where it is necessary to ensure knowledge sharing between partners, while ensuring the protection of sensitive data at the same time.
more info
year 2022/2023
- Faculty of Informatics and Information Technologies of STU in Bratislava (program: Applied Informatics)
- Faculty of Electrical Engineering and Informatics, Technical Uni of Košice (FEI TUKE, program: Informatics)
Many modern-world problems, which we want to solve with the help of AI, require direct access to sensitive data such as personal, medical, business, or security information. In many cases, getting access to such information is nearly impossible due to the protection requirements. From this point, several questions are raised, such as how to avoid sensitive information disclosure in the data science process or moreover, how can we model sensitive data without direct access to them. The topic of the work is focused on collaborative (federated) learning, where it is necessary to ensure knowledge sharing between partners, while ensuring the protection of sensitive data at the same time.
more info
year 2022/2023