Parallel Session on Privacy-preserving technologies – a key enabler of big data for AI
Modern AI applications crucially depend on large volumes of real-world data, including data about natural persons. However, the use of personal data is associated with data protection challenges. Privacy-preserving technologies are being developed to cope with these challenges. This is a hot topic because
- AI applications are spreading rapidly, opening new possibilities for technology, business, and society.
- There is a growing concern about the use of personal data in AI applications. Misalignment with current and upcoming EU legislation (e.g., GDPR, ePrivacy) threatens to hamper the adoption of AI.
- Privacy-preserving AI and big data applications are in the focus of several ongoing or recently finished EU projects. Dissemination of the projects’ results and cross-fertilization among the projects would be highly beneficial.
The aim of this session is to discuss cutting-edge advances in privacy-preserving technologies and how they foster the use of big data for AI. The session will allow current and recently finished EU projects (BDV PPP and beyond) to present and discuss their use of privacy-preserving technologies to protect data for AI.
- Zoltan Mann (University Duisburg-Essen, BDVA)
Towards robust privacy preserving of data and digital sovereignty in European data spaces: examples from EU research projects
- Alberto Crespo & Juan Carlos Perez Baun (Atos)
MOSAICrOWN: Multi-Owner data Sharing for Analytics and Integration respecting Confidentiality and OWNer control
- Pierangela Samarati (University Milano)
Musketeer – a scalable, secure and privacy aware architecture
- Mark Purcell (IBM)
Risk Analysis of Inference Attacks on IoT Wearables
- Sebastian Pape (University Frankfurt)
GDPR Certification applicability to Artificial Intelligence
- Sébastien Ziegler (Mandat International)