SPINDLE

SPINDLE

Scalable, privacy-preserving, distributed learning on distributed datasets

SPINDLE allows to train and evaluate generalized linear models on datasets that are stored at different data providers who don't want to share the original data.

Homomorphic EncryptionSecure Multi-Party Computation
Key facts
Maturity
Support
C4DT
Retired
Lab
Unknown
  • C4DT work
  • Technical
  • Research papers
  • Presentation
  • App
  • Demo
  • Details
Status: Retired
Timeline: 2021/Q1 created Spindle demonstrator

Laboratory for Data Security

Laboratory for Data Security

Prof. Jean-Pierre Hubaux

Over the last 15 years, the Laboratory for Data Security has pioneered the areas of security and privacy in personalized health and mobile/wireless networks as well as tackled interpersonal privacy issues. On the first topic, they collaborate extensively with hospitals. Their core competencies are in applied cryptography, data protection techniques such as differential privacy, and wireless networking. Their research is funded by the Strategic Focus Area “Personalized Health and Related Technologies” of the ETH Domain, the Swiss Data Science Center and the Swiss National Science Foundation.

This page was last edited on 2022-09-28.