Name:
Drynx
Description:
Decentralized, secure, verifiable system for statistical queries and machine learning on distributed datasets
Professor — Lab:
Jean-Pierre HubauxLaboratory for Data Security
Contacts:
David Froelicher Joao de Sá

Technical description:
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.
Papers:
Project status:
unknown — entered showcase: 2019-03-18 — entry updated: 2022-09-28

Factory Development:
2020/Q1 created demonstrator for SwissRe
C4DT Contact:
Valérian Rousset

Code quality:
Prototype
Project type:
Application
Programming language:
GoLang
License:
other
Notes:
Transfered to TuneInsight