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/Unstructured Data, Image/Video Data).

In particular, we have and continue to study problems of data privacy centered on the following major research themes:

  1. Quantifying risk of exposure of sensitive information
  2. Developing and evaluating methods to reduce this risk
  3. Developing privacy exposure risk scoring mechanisms for evaluating data
  4. Developing Green-IT privacy solutions
  5. Improving users' understanding of privacy and developing automated methods to aid users in adopting safer privacy attitudes

Members of the team develop and use a diverse array of algorithms and methodologies including information discovery algorithms, data transformation approaches, machine learning models, qualitative and quantitative research methods, and Low-power/processing methods.

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.