FENTEC: Increasing Trustworthiness of ICT solutions with Functional Encryption

Francisco J. G. Gala

Francisco Gala works for Atos Research and Innovation (ARI) as Head of the Secure Societies and Societal Transformation Unit. He holds a master’s degree in environmental sciences and an MBA. He has 17-year multidisciplinary experience in the UK and Spain, having worked in different key sectors such as ICT, engineering, environmental protection and utilities. Francisco joined Atos in 2016 and he has coordinated and worked in several EC innovation projects, including DRIVER, RESISTAND, STRATEGY and FENTEC.


FENTEC’s core objective is to develop new Functional Encryption (FE) as an efficient alternative to the all-or-nothing approach of traditional encryption, enabling partial views over encrypted data and effectively enhancing security of complex systems by compartmentalization of data or computation over the data. It addresses the challenge to design general purpose FE supporting the decryption of efficiently computable functions with novel cryptographic algorithms and hardware concepts within practical metrics, balancing the individual functional, efficiency and security requirements.

The project brings together a team of cryptographers, software experts, hardware specialists and IT industry representatives with the aim of developing efficient and innovative FE systems which are application oriented and can be used in a wide range of scenarios. FENTEC will design, develop, implement and demonstrate the usefulness and real-life applications of FE, producing tangible advantages for the whole ICT industry and for stakeholders that need to operate in environments where data confidentiality and privacy is needed, but partial access to the data through external parties is unavoidable. The security, efficiency, expressiveness and versatility of the new FE approach will be showcased in three use-cases:

  1. Privacy-preserving digital currency, enforcing flexible auditing models

  2. Data Collection and IoT Local Decision Making

  3. Privacy-Preserving Statistical Analysis