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, Multimedia 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 Transformations/Distortions 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 using our work as a benchmark please do not hesitate to contact us. We also are open to joint research collaborations on exciitng topics in the data privacy field.