Stephan Krenn

Stephan Krenn, Doryan Lesaignoux, Sebastian Ramacher

Electronic identities (eIDs) are crucial in an increasingly digitalized environment. Pseudonyms, as offered by Austria's governmental sector-specific personal identifiers (bPks), can significantly improve privacy by ensuring that personal data is not universally traceable across public services and private companies. However, the current architecture comes with several challenges regarding availability, privacy, and authenticity, due to a fully centralized design. This paper proposes bPk#, a distributed architecture to address these issues, reducing reliance on the central authority, while still providing all functional requirements to the existing bPk system. In particular, users are delegated the rights to compute their own pseudonyms, thereby minimizing metadata revealed to the central authority, while (subsets of) service providers may receive the right to compute pseudonyms only within their own domain, thereby reducing the availability needs of the central authority. To the best of our knowledge, we provide the first formal framework for such delegatable pseudonym systems, together with a generic construction for which we provide formal security proofs. Furthermore, we propose a concrete instantiation of our construction, together with a reference implementation demonstrating the practical efficiency.

Stephan Krenn, Omid Mir, Thomas Lorünser et al.

Secure long-distance communication in quantum key distribution (QKD) networks depends on trusted repeater nodes along the entire transmission path. Consequently, these nodes will be subject to strict auditing and certification in future large-scale QKD deployments. However, trust must also extend to the network operator, who is responsible for fulfilling contractual obligations -- such as ensuring certified devices are used and transmission paths remain disjoint where required. In this work, we present a path validation protocol specifically designed for QKD networks. It enables the receiver to verify compliance with agreed-upon policies. At the same time, the protocol preserves the operator's confidentiality by ensuring that no sensitive information about the network topology is revealed to users. We provide a formal model and a provably secure generic construction of the protocol, along with a concrete instantiation. For long-distance communication involving 100 nodes, the protocol has a computational cost of 1-2.5s depending on the machine, and a communication overhead of less than 70kB - demonstrating the efficiency of our approach.

Margherita Cozzolino, Stephan Krenn, Thomas Lorünser

While QKD ensures information-theoretic security at the link level, real-world deployments depend on trusted repeaters, creating potential vulnerabilities. In this paper, we thus introduce a topology-hiding connectivity assurance protocol to enhance trust in quantum key distribution (QKD) network infrastructures. Our protocol allows network providers to jointly prove the existence of a secure connection between endpoints without revealing internal topology details. By extending graph-signature techniques to support multi-graphs and hidden endpoints, we enable zero-knowledge proofs of connectivity that ensure both soundness and topology hiding. We further discuss how our approach can certify, e.g., multiple disjoint paths, supporting multi-path QKD scenarios. This work bridges cryptographic assurance methods with the operational requirements of QKD networks, promoting verifiable and privacy-preserving inter-network connectivity.

Karl Koch, Stephan Krenn, Donato Pellegrino et al.

Data markets have the potential to foster new data-driven applications and help growing data-driven businesses. When building and deploying such markets in practice, regulations such as the European Union's General Data Protection Regulation (GDPR) impose constraints and restrictions on these markets especially when dealing with personal or privacy-sensitive data. In this paper, we present a candidate architecture for a privacy-preserving personal data market, relying on cryptographic primitives such as multi-party computation (MPC) capable of performing privacy-preserving computations on the data. Besides specifying the architecture of such a data market, we also present a privacy-risk analysis of the market following the LINDDUN methodology.

Stefan Ruehrup, Stephan Krenn

With geographic message dissemination, connected vehicles can be served with traffic information in their proximity, thereby positively impacting road safety, traffic management, or routing. Since such messages are typically relevant in a small geographic area, servers only distribute messages to affected vehicles for efficiency reasons. One main challenge is to maintain scalability of the server infrastructure when collecting location updates from vehicles and determining the relevant group of vehicles when messages are distributed to a geographic relevance area, while at the same time respecting the individual user's privacy in accordance with legal regulations. In this paper, we present a framework for geographic message dissemination following the privacy-by-design and privacy-by-default principles, without having to accept efficiency drawbacks compared to conventional server-client based approaches.