• Zero-trust relay for QKD networks using homomorphic encryption on conventional hardware • External QRNG integration to enhance crypto-agility in embedded random key generation • An extra confidentiality layer through the integration of cryptographic hardware Quantum key distribution (QKD) enables unconditionally secure symmetric key exchange between parties. However, terrestrial fibre-optic links face inherent distance constraints due to quantum signal degradation. Traditional solutions to overcome these limits rely on trusted relay nodes, which perform intermediate re-encryption of keys using one-time pad (OTP) encryption. This approach, however, exposes keys as plaintext at each relay, requiring significant trust and stringent security controls at every intermediate node. These “trusted” relays become a security liability if compromised. To address this issue, we propose a zero-trust relay design that applies fully homomorphic encryption (FHE) to perform intermediate OTP re-encryption without exposing plaintext keys, effectively mitigating the risks associated with potentially compromised or malicious relay nodes. Additionally, the architecture enhances crypto-agility by incorporating external quantum random number generators, thus decoupling key generation from specific QKD hardware and reducing vulnerabilities tied to embedded key-generation modules. The solution is designed with the existing European Telecommunication Standards Institute (ETSI) QKD standards in mind, enabling straightforward integration into current infrastructures. Its feasibility has been successfully demonstrated through a hybrid network setup combining simulated and commercially available QKD equipment. The proposed zero-trust architecture thus significantly advances the scalability and practical security of large-scale QKD networks, greatly reducing reliance on fully trusted infrastructure.

Federated Learning emerged as a promising solution to enable collaborative training between organizations while avoiding centralization. However, it remains vulnerable to privacy breaches and attacks that compromise model robustness, such as data and model poisoning. This work presents PRoT-FL, a privacy-preserving and robust Training Manager capable of coordinating different training sessions at the same time. PRoT-FL conducts each training session through a Federated Learning scheme that is resistant to privacy attacks while ensuring robustness. To do so, the model exchange is conducted by a “Private Training Protocol” through secure channels and the protocol is combined with a public blockchain network to provide auditability, integrity and transparency. The original contribution of this work includes: (i) the proposal of a “Private Training Protocol” that breaks the link between a model and its generator, (ii) the integration of this protocol into a complete system, PRoT-FL, which acts as an orchestrator and manages multiple trainings and (iii) a privacy, robustness and performance evaluation. The theoretical analysis shows that PRoT-FL is suitable for a wide range of scenarios, being capable of dealing with multiple privacy attacks while maintaining a flexible selection of methods against attacks that compromise robustness. The experimental results are conducted using three benchmark datasets and compared with traditional Federated Learning using different robust aggregation rules. The results show that those rules still apply to PRoT-FL and that the accuracy of the final model is not degraded while maintaining data privacy.

Big data has proven to be a very useful tool for companies and users, but companies with larger datasets have ended being more competitive than the others thanks to machine learning or artificial intelligence. Secure multi-party computation (SMPC) allows the smaller companies to jointly train arbitrary models on their private data while assuring privacy, and thus gives data owners the ability to perform what are currently known as federated learning algorithms. Besides, with a blockchain it is possible to coordinate and audit those computations in a decentralized way. In this document, we consider a private data marketplace as a space where researchers and data owners meet to agree the use of private data for statistics or more complex model trainings. This document presents a candidate architecure for a private data marketplace by combining SMPC and a public, general-purpose blockchain. Such a marketplace is proposed as a smart contract deployed in the blockchain, while the privacy preserving computation is held by SMPC.

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The recent increase in reported incidents of security breaches compromising users' privacy call into question the current centralized model in which third-parties collect and control massive amounts of personal data. Blockchain has demonstrated that trusted and auditable computing is possible using a decentralized network of peers accompanied by a public ledger. Furthermore, Homomorphic Encryption (HE) guarantees confidentiality not only on the computation but also on the transmission, and storage processes. The synergy between Blockchain and HE is rapidly increasing in the computing environment. This research proposes a privacy-enhancing distributed and secure protocol for data aggregation backboned by Blockchain and HE technologies. Blockchain acts as a distributed ledger which facilitates efficient data aggregation through a Smart Contract. On the top, HE will be used for data encryption allowing private aggregation operations. The theoretical description, potential applications, a suggested implementation and a performance analysis are presented to validate the proposed solution.

This paper describes an educational tool to facilitate the study of digital signal processing. This application offers a GUI support for integrating signal processing demos, using plugins that execute Octave/Matlab functions. On the one hand, this application permits students to change parameters for several examples, classified in categories such as modulations, filter design, speech and image signals analysis and processing. On the other hand, it offers the possibility of easily implementing two kinds of new signal processing plugins, XML based and Java based.