SPRITE: Safe and Privacy Preserving Data Publishing

Welcome to the homepage of the SPRITE (Safe and privacy Preserving Data Publishing) team.

We are embedded within the Internet Technologies and Systems chair at the Hasso Plattner Institute. The SPRITE Team focuses on problems of designing secure and privacy preserving algorithms to enable data sharing on web-based platforms. Notably we are interested in the issues arise in enabling privacy preserving machine learning on various datasets (e.g. High Dimensional Data, Textual Data, Image Data).


In particular, we have and continue to study mechanisms such as (1) Quasi-Identifier Discovery, (2) User-Centric Privacy (3) Outlier/Anomaly Detection, (4) Image Privacy and (5) Automation based on learned behaviours to support human-centered privacy decision making on web applications. We study the impact transformation measures such as distortion, generalization, and suppression have on enabling the generation of privacy preserving data, as well as potential adversarial exploits to subverting the privacy goals of the generated data.


Our research activities are supported by lectures, and seminars, and we offer opportunities for graduate research on topics related to the area of ​​data privacy. If you are interested in joining our team (Research Assistant/Doctoral Student, Master's Student, and/or Student Assistant) please do not hesitate to contact us. We are also open to joint research collaborations on excitng topics in the data privacy field.



Our Team

Includes PhD students, Masters students, and student assistants, and is lead by Dr. Anne Kayem. If you are interested in joining our team, please contact us.