TITAN will enrich the EOSC Interoperability Framework (IF) with a software platform solution for confidential data collaboration and secure and privacy-preserving data processing. The platform will enable access to sensitive data sets from public entities and government agencies and will be compatible by design with the EOSC IF on the technical, semantic, organisational and legal layers. To promote community adoption of TITAN’s open-source software artefacts, the solution will be practically demonstrated in several vertical cross-border scenarios - notably in the public administration and healthcare sector

The main objective of GLocalFlex is to mobilise demand-response solutions & services in replicated manner for prompt horizontal scaling of flexible local energy systems (LES) by means of easy access and low barrier energy flexibility markets to increase the participation of the consumers across all energy-sectors. The GLocalFlex approach promotes viable interoperable solutions and products at all levels of the grid (consumers, producers, retailers, aggregators & market) by selecting modular standards and tools during development. It allows the overall system of systems to be 1) flexible by means of energy use to provide quality services to grid and 2) flexible by means of its own evolution (to change, upgrade or integrate several appliances, consumers and microgrids to create complex but viable cross-sectoral energy ecosystems). The GLocalFlex concept will allow any Local Energy System (LES), Positive Energy District(PED), community, or appliance to evolve in a consistent way and offer flexibility using suitable hardware add-ons in a cost-effective and systematic manner to expose their full flexible potential. GLocalFlex concept can adopt to any LES size, availability of local resources and regulations. This project will demonstrate near real time, large scale, flexibility trading in six different field locations in Europe. It has consumer and system centric pilots that develop flexibility services and replicate them within the pilot group. The GLocalFlex aims to have completely automated, machine-to-machine flexibility trading and flexibility order execution in LES by means of open, interoperable energy flexibility market and open standard based IT-tools that allows more flexibility consumers to join the market. Therefore, GLocalFlex will push forward the overall integration of disruptive renewable technologies and enhancing the stability of the grid at the same time.

Availability of large volumes of user data combined with tailored statistical analysis present a unique opportunity for organizations across the spectrum to adapt and finetune their services according to individual needs. Having shown remarkable results in analyzing user data, machine learning models attracted global adulation and are applied in a plethora of applications including medical diagnostics, pattern recognition, and threat intelligence. However, such service improvements and personalization based on user data analysis come at the heavy cost of privacy loss. Furthermore, practice showed that systems that use such models incorporate proxies that are often inexact, biased and often unfair. In HARPOCRATES, we focus on setting the foundations of digitally blind evaluation systems that will, by design, eliminate proxies such as geography, gender, race, and others and eventually have a tangible impact on building fairer, democratic and unbiased societies. To do so, we plan to design several practical cryptographic schemes (Functional Encryption and Hybrid Homomorphic Encryption) for analyzing data in a privacy-preserving way. Besides processing statistical data in a privacy-preserving way, we also aim to enable a richer, more balanced and comprehensive approach where data analytics and cryptography go hand in hand with a shift towards increased privacy. In HARPOCRATES we will first show how to effectively combine cryptography with the principles of differential privacy to secure and privatise databases. Next, we will build privacy-preserving machine learning models able to classify encrypted data by performing high accuracy predictions directly on ciphertexts across federated data spaces. Finally, to demonstrate how these solutions respond to users’ needs, we will implement two real-world cross-border data sharing scenarios related to health data analysis for sleep medicine and threat intelligence for local authorities.