Institute of Distributed Systems

For a quick chat, feel free to drop by at my office spontaneously. If I'm not there or you prefer a longer meeting, please reach out via email first.

Lukas Pietzschmann

Lukas Pietzschmann received his Bachelor's degree in computer science from Aalen University in 2022, followed by his Master's degree from Ulm University in 2025. He is currently employed as a research assistant at the Institute of Distributed Systems.

Lukas is exploring potential privacy risks associated with public datasets. Currently, he's particularly interested in possible attack vectors and the potential for re-identification of individuals in datasets stemming from empirical research.

Besides that, he's also working on the BwNet2.0 project, which is concerned with the development of various parts of the Belwü network. His main focus is in the monitoring, collection, and provision of network data for the purpose of research and analysis.

Teaching

Labs

Seminars

Open Theses and Projects

F. Wenzl, „The Evolution of Privacy Dark Patterns,“ Masterarbeit, B. Erb and L. Pietzschmann (Betreuung), F. Kargl (Prüfer), Inst. of Distr. Sys., Ulm Univ., 2025 – reserved.
Motivated by the need to combat manipulative practices that trick users into sharing more personal data than intended, this thesis investigates the evolution of privacy dark patterns. As data collection becomes more central online, this study conducts a systematic literature review to categorize these patterns, identify countermeasures, and analyze how they have changed in response to legal regulations and shifting public awareness.
„From data points to identities: Assessing privacy vulnerabilities in empirical research datasets,“ Bachelorarbeit oder Masterarbeit, L. Pietzschmann (Betreuung), F. Kargl (Prüfer), Inst. of Distr. Sys., Ulm Univ., 2025 – reserved.
Publishing datasets is a common practice in empirical research. However, the risk of de-anonymization of individuals in these datasets is a significant concern, especially when the data is highly sensitive. This thesis aims to investigate the presence of privacy related vulnerabilities in a large collection of datasets. The thesis will focus on quantifying the extent of vulnerabilities and attempt to categorize them into distinct types.
L. Völk, „Evaluating de-anonymization attacks,“ Bachelorarbeit, B. Erb and L. Pietzschmann (Betreuung), F. Kargl (Prüfer), Inst. of Distr. Sys., Ulm Univ., 2025 – reserved.
This thesis investigates the effectiveness of various anonymization techniques against de-anonymization attacks. Recognizing the difficulty of evaluating these attacks with real-world data due to the lack of "ground truth", the work proposes using synthetically generated datasets where all information is known, allowing for objective measurement of attack success rates. The research will compare k-anonymity, l-diversity, and t-closeness against different attack models, including linkage attacks, to understand their robustness in practical data protection scenarios.