Matteo Nardelli

Matteo Nardelli, Francesco De Sclavis, Michela Iezzi

Crypto-assets and central bank digital currencies (CBDCs) are reshaping how value is exchanged in distributed computing environments. These systems combine cryptographic primitives, protocol design, and system architectures to provide transparency and efficiency while raising critical challenges around privacy and regulatory compliance. This survey offers a comprehensive overview of privacy-preserving digital payment systems, covering both decentralized ledger systems and CBDCs. We present a taxonomy of privacy goals -- including anonymity, confidentiality, unlinkability, and auditability -- and map them to the cryptographic primitives, protocols, and system architectures that implement them. Our work adopts a design-oriented perspective, linking high-level privacy objectives to concrete implementations. We also trace the evolution of privacy-preserving digital payment systems through three generations, highlighting shifts from basic anonymity guarantees toward more nuanced privacy-accountability trade-offs. Finally, we identify open challenges, motivating further research into architectures and solutions that balance strong privacy with real-world auditability needs.

Michael Borkowski, Walid Fdhila, Matteo Nardelli et al.

Traditionally, research in Business Process Management has put a strong focus on centralized and intra-organizational processes. However, today's business processes are increasingly distributed, deviating from a centralized layout, and therefore calling for novel methodologies of detecting and responding to unforeseen events, such as errors occurring during process runtime. In this article, we demonstrate how to employ event-based failure prediction in business processes. This approach allows to make use of the best of both traditional Business Process Management Systems and event-based systems. Our approach employs machine learning techniques and considers various types of events. We evaluate our solution using two business process data sets, including one from a real-world event log, and show that we are able to detect errors and predict failures with high accuracy.