Secret Smart Contracts in Hierarchical Blockchains

This article presents the results of an implementation of a new platform based on swarm communication and executable choreographies. In our research of executable choreographies, we have come up with a more general model to implement smart contracts and a generic architecture of systems using hierarchical blockchain architecture. The novel concepts of secret smart contract and near-chain are introduced. The near-chain approach presents a new method to extend the hierarchical blockchain architecture and to improve performance, security and privacy characteristics of general blockchain-based systems. As such, we are subsequently defining and explaining why any extension of blockchain architectures should revolve around three essential dimensions: trustlessness, non-repudiation and tamper resistance. The hierarchical blockchain approach provides a novel perspective, as well as establishing off-chain storages (near-chains) as special types of hierarchical blockchains stored in a distributed file system. Furthermore, we are providing solutions to the difficult blockchain concerns regarding scalability, performance and privacy issues

Introducing the TRUMPET project: TRUstworthy Multi-site Privacy Enhancing Technologies

International audienceThis paper is an overview of the EU-funded project TRUMPET (https://trumpetproject.eu/), and gives an outline of its scope and main technical aspects and objectives. In recent years, Federated Learning has emerged as a revolutionary privacy-enhancing technology. However, further research has cast a shadow of doubt on its strength for privacy protection. The goal of TRUMPET is to research and develop novel privacy enhancement methods for Federated Learning, and to deliver a highly scalable Federated AI service platform for researchers, that will enable AI-powered studies of siloed, multi-site, crossdomain, cross-border European datasets with privacy guarantees that follow the requirements of GDPR. The generic TRUMPET platform will be piloted, demonstrated and validated in the specific use case of European cancer hospitals, allowing researchers and policymakers to extract AI-driven insights from previously inaccessible cross-border, cross-organization cancer data, while ensuring the patients' privacy

Introducing the TRUMPET project: TRUstworthy Multi-site Privacy Enhancing Technologies

International audienceThis paper is an overview of the EU-funded project TRUMPET (https://trumpetproject.eu/), and gives an outline of its scope and main technical aspects and objectives. In recent years, Federated Learning has emerged as a revolutionary privacy-enhancing technology. However, further research has cast a shadow of doubt on its strength for privacy protection. The goal of TRUMPET is to research and develop novel privacy enhancement methods for Federated Learning, and to deliver a highly scalable Federated AI service platform for researchers, that will enable AI-powered studies of siloed, multi-site, crossdomain, cross-border European datasets with privacy guarantees that follow the requirements of GDPR. The generic TRUMPET platform will be piloted, demonstrated and validated in the specific use case of European cancer hospitals, allowing researchers and policymakers to extract AI-driven insights from previously inaccessible cross-border, cross-organization cancer data, while ensuring the patients' privacy