Brokerage and market platform for personal data


Horizon 2020


01. 12. 2019 - 30. 11. 2022


Miha Stopar
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871473.

Secure and privacy preserving platform aims to bring personal data sharing and trading at a level of maturity that does not yet exist, by leveraging on:

1) the emerging paradigm of self-sovereign identity built upon a stack of distributed ledger technologies (multi-ledger) which ensures future compatibility with different specific blockchain implementations for identity management. It will provide a decentralized user-centric approach on personal data sharing and proving that it can incorporate the trust and security assurance levels deriving claims from national identity schemas (eIDAS-compliant);

2) tested data marketplace technologies which support data sharing as well as aggregated data sharing;

3) a set of different data protection techniques based on advanced crypto tools (P/F/HE, FE, MPC, ABE…) coupled with privacy preserving (AI/ML) analytics, featuring management of privacy / utility trade-offs and metadata privacy.

KRAKEN will provide market-ready tools and services with industrial strength and suitable privacy metrics that will be conveyed to data subjects with high usability.

The project proposes an unprecedented approach, creating an alternative to mainstream paradigms while fully granting the privacy and self-sovereignity of the data subject. It enables advanced, convenient data sharing control relying on innovative end-to-end encryption.

The use of sophisticated proxy cryptography schemes grants data subjects with unprecedented control over their data: even the cloud provider (data processor) can’t access the data in plain-text, so access to personal data is protected.

XLAB's role

XLAB will implement privacy-preserving data analytics algorithms by using functional encryption and secure multi-party computation cryptographic schemes. Mechanisms for secure clustering, aggregation, and statistical evaluations (e.g., mean, variance, quantiles) of sensitive data will be enabled without revealing data in the clear. The work will present a continuation of the libraries and privacy-enhanced data analysis mechanisms developed in FENTEC [1][2][3][4][5].