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Jeff Nijsse

Publications and source records attributed to Jeff Nijsse.

3 recordsLinked to original sources

Central Bank Digital Currencies: Where is the Privacy, Technology, and Anonymity?

In an age of financial system digitisation and the increasing adoption of digital currencies, Central Bank Digital Currencies (CBDCs) have emerged as a focal point for technological innovation. Privacy compliance has become a key factor in the successful design of CBDCs, extending beyond technical requirements to influence legal requirements, user trust, and security considerations. Implementing Privacy-Enhancing Technologies (PETs) in CBDCs requires an interdisciplinary approach, however, the lack of a common understanding of privacy and the essential technological characteristics restricts progress. This work investigates: (1) How privacy can be defined within the framework of CBDCs and what implications does this definition have for CBDCs design? and (2) Which PETs can be employed to enhance privacy in CBDC design? We propose a comprehensive definition for privacy that is mapped to the cryptographic landscape for feature implementation. The research is validated against case studies from 20 current CBDCs. The study shows that comprehensive privacy can be designed in the proposal stage, but that privacy does not reach the launched version of the CBDC. A failure analysis of abandoned privacy pilots identifies four root causes of this research-to-launch gap: regulatory visibility requirements, computational overhead, liability allocation, and institutional incentives.

cs.CR

Normalisation and Initialisation Strategies for Graph Neural Networks in Blockchain Anomaly Detection

Graph neural networks (GNNs) offer a principled approach to financial fraud detection by jointly learning from node features and transaction graph topology. However, their effectiveness on real-world anti-money laundering (AML) benchmarks depends critically on training practices such as specifically weight initialisation and normalisation that remain underexplored. We present a systematic ablation of initialisation and normalisation strategies across three GNN architectures (GCN, GAT, and GraphSAGE) on the Elliptic Bitcoin dataset. Our experiments reveal that initialisation and normalisation are architecture-dependent: GraphSAGE achieves the strongest performance with Xavier initialisation alone, GAT benefits most from combining GraphNorm with Xavier initialisation, while GCN shows limited sensitivity to these modifications. These findings offer practical, architecture-specific guidance for deploying GNNs in AML pipelines for datasets with severe class imbalance. We release a reproducible experimental framework with temporal data splits, seeded runs, and full ablation results.

cs.LG

Towards a Structural Equation Model of Open Source Blockchain Software Health

The widespread use of GitHub among software developers as a communal platform for coordinating software development has led to an abundant supply of publicly accessible data. Ever since the inception of Bitcoin, blockchain teams have incorporated the concept of open source code as a fundamental principle, thus making the majority of blockchain-based projects' code and version control data available for analysis. We define health in open source software projects to be a combination of the concepts of sustainability, robustness, and niche occupation. Sustainability is further divided into interest and engagement. This work uses exploratory factor analysis to identify latent constructs that are representative of general public interest or popularity in software, and software robustness within open source blockchain projects. We find that interest is a combination of stars, forks, and text mentions in the GitHub repository, while a second factor for robustness is composed of a criticality score, time since last updated, numerical rank, and geographic distribution. Cross validation of the dataset is carried out with good support for the model. A structural model of software health is proposed such that general interest positively influences developer engagement, which, in turn, positively predicts software robustness. The implications of structural equation modelling in the context of software engineering and next steps are discussed.

cs.SE