Drynx
Decentralized, secure, verifiable system for statistical queries and machine learning on distributed datasets
Drynx allows to create privacy-preserving queries on encrypted datasets that are stored at different data providers who don't want to share the original data. Different types of statistical queries are possible, like average, standard deviation, linear and logistic regressions - all using homomorphic encryption, which means that the data is never shared in cleartext.
Homomorphic EncryptionSecure Multi-Party Computation
Key facts
Maturity
Support
C4DT
Lab
Key facts
Maturity
Support
C4DT
Lab
- C4DT work
- Technical
- Research papers
- Presentation
- Demo
Status: Retired
Timeline: 2020/Q1 created demonstrator for SwissRe
Timeline: 2020/Q1 created demonstrator for SwissRe
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.
This page was last edited on 2022-09-28.