Privacy-preserving Image Data Generation

August 24th, 2022

Thesis Type Master
Status Open
Presentation room Seminar room I5 6202
Supervisor(s) Stefan Decker
Advisor(s) Yongli Mou

Synthetic data is an efficient way to protect data privacy [1]. However, recent research shows that the synthetic data drawn from the original data distribution cannot provide free privacy [1]. Therefore, privacy enhancement techniques need to be taken into consideration when we design data synthesis techniques. The privacy-preserving solutions always consume the model utility. It is challenging to design algorithms that provide a strong privacy guarantee along with high data utility.

In this thesis, the student should review the state-of-the-art techniques in privacy protection and data synthesis, and propose a privacy-preserving image synthesis algorithm with high data utility. Specifically, the thesis aims to design a differential privacy-based data synthesis technique.

The thesis provides a potential opportunity to do an internship at Huawei Munich Research Center.

If you are interested in this thesis, do not hesitate to contact us via

[1] Chen, Jia-Wei, et al. “DPGEN: Differentially Private Generative Energy-Guided Network for Natural Image Synthesis.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.

[2] Stadler, Theresa, Bristena Oprisanu, and Carmela Troncoso. “Synthetic data–anonymisation groundhog day.” 31st USENIX Security Symposium (USENIX Security 22). 2022.

[3] Chen D, Yu N, Zhang Y, Fritz M. Gan-leaks: A taxonomy of membership inference attacks against generative models. InProceedings of the 2020 ACM SIGSAC conference on computer and communications security 2020 Oct 30 (pp. 343-362).


Knowledge about Machine Learning
Programming language – Python
Deep Learning Framework – PyTorch