The deluge of big data, accompanied by developments in software and hardware technologies leveraging them, has created new opportunities for research and industry. The main challenges, though, faced by researchers and service providers working with personal data, are stemming from the fact that these data need to be processed in a privacy-preserving way, as they contain sensitive information. Although several technologies have been developed to facilitate the processing of data while preserving privacy, they have not made significant inroads into real use cases, due to several reasons.

ENCRYPT will develop 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. Within this framework, a recommendation engine for citizens and end-users will be developed, providing them with personalised suggestions on privacy preserving technologies depending on the sensitivity of data and the accepted trade-off between the degree of security and the overall system performance.

The ENCRYPT framework will be designed taking into consideration the needs and preferences of relevant actors, and will be validated in a comprehensive, 3-phase validation campaign, comprising i) in-lab validation tests, ii) use cases provided by consortium partners in three sectors, namely the health sector, the cybersecurity sector, and the finance sector, that include cross-border processing of data, and iii) external use cases including privacy preserving computations on federated medical datasets.

MKLab leads the research and development of privacy-supporting technologies and the Use Cases implementation. More specifically, MKLab is responsible for the development of advanced data processing techniques required for the proper application of the Privacy-Preserving Technologies (PPTs) to be implemented by the ENCRYPT project and for the development of the User Interface (UI). In addition, MKLab acts as a Use Case provider by developing the necessary tools for the extraction of Cyber Threat Intelligence (CTI) from the available data, as well as for the correlation and enrichment of the extracted CTI.






  • Kavallieros Dimitris
  • Tsikrika Theodora
  • Vrochidis Stefanos
  • Kompatsiaris Ioannis