A Practical and Scalable Privacy-preserving Framework
Creators
- 1. Diadikasia Business Consulting S.A, Athens, Greece
- 2. TRUSTUP, Naples, Italy
- 3. Tilburg Institute for Law, Technology, and Society Tilburg Law School, Tilburg, The Netherlands
- 4. Department of Computer Science, The University of Manchester, Manchester, United Kingdom
- 5. EXUS, Athens, Greece
- 6. Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
- 7. School of Informatics Aristotle University of Thessaloniki, Thessaloniki, Greece
- 8. Eight Bells Ltd, Athens, Greece
- 9. Information Technologies Institute, CERTH, Thessaloniki, Greece
- 10. R&D Labs Engineering Ingegneria Informatica, Rome, Italy
- 11. Universite Paris-Saclay, CEA, List, Palaiseau, France
Description
ENCRYPT is an EU funded research initiative, working towards the development of a scalable, practical, adaptable privacy-preserving framework, allowing researchers and developers to process data stored in federated cross-border data spaces in a GDPR-compliant way. ENCRYPT proposes an intelligent and user-centric platform for the confidential processing of privacy-sensitive data via configurable, optimizable, and verifiable privacy-preserving techniques. Hence, ENCRYPT builds on top of cutting-edge technologies such as Fully Homomorphic Encryption, Secure Multi-Party Computation, Differential Privacy, Trusted Execution Environment, GPU acceleration, knowledge graphs, and AI-based recommendation systems, making them configurable in terms of security and, most importantly, performance. The ENCRYPT framework is being designed taking into consideration the needs and preferences of relevant actors and will be validated in realistic use cases provided by consortium partners in three sectors, namely healthcare (oncology domain), fintech, and cyber threat intelligence domain. This position paper provides an overview of ENCRYPT by presenting project objectives, use cases, and technology pillars.
Files
ENCRYPT joint paper semifinal.pdf
Files
(122.5 kB)
Name | Size | Download all |
---|---|---|
md5:5d45cee85f760c2b8cd68536bb3ff144
|
122.5 kB | Preview Download |