Digital identity wallets promise significant advancements in digital identity management by offering users a high degree of convenience, security, and control over their data disclosure. However, there is also criticism regarding their privacy guarantees, especially when used in regulated use cases that require high levels of assurance on the correctness and binding of a legal identity. In this paper, we present a comprehensive privacy model and analysis of one of the most prominent digital wallets – the European Digital Identity Wallet (EUDIW) – as specified by the Architecture and Reference Framework (ARF) and the eIDAS 2.0 regulation. We employ a suite of qualitative privacy risk assessment methods to systematically map and evaluate information flows in three key use cases. Our analysis identifies multiple privacy risks – including linkability, identifiability, and excessive attribute data disclosure – and reveals that although the ARF is designed to comply with privacy-by-design principles, inherent design choices, such as the reliance on SD-JWT and mDOC data formats, as well as the concept of a Wallet Unit Attestation (WUA), retain risks to user privacy. Building on our findings, we then highlight how advanced Privacy-Enhancing Technologies (PETs), such as (general-purpose) Zero-Knowledge Proofs (ZKPs), can reduce or mitigate some of these risks.

Hierarchical Text Classification (HTC) involves assigning documents to labels organized within a taxonomy. Most previous research on HTC has focused on supervised methods. However, in real-world scenarios, employing supervised HTC can be challenging due to a lack of annotated data. Moreover, HTC often faces issues with large label spaces and long-tail distributions. In this work, we present Knowledge Graphs for zero-shot Hierarchical Text Classification (KG-HTC), which aims to address these challenges of HTC in applications by integrating knowledge graphs with Large Language Models (LLM) to provide structured semantic context during classification. Our method retrieves relevant subgraphs from knowledge graphs related to the input text using a Retrieval-Augmented Generation (RAG) approach, thereby augmenting the model’s understanding of label semantics at various hierarchy levels. We evaluate KG-HTC on three open-source HTC datasets: WoS, Dbpedia, and Amazon. Our experimental results show that KG-HTC significantly outperforms three baselines in the strict zero-shot setting, particularly achieving substantial improvements at deeper levels of the hierarchy. This evaluation demonstrates the effectiveness of incorporating structured knowledge into LLMs to address HTC’s challenges in large label spaces and long-tailed label distributions. Our code is available at: https://github.com/QianboZang/KG-HTC.

As human-agent interactions become increasingly prevalent, designing systems that foster trust, transparency, and user autonomy is crucial. This paper introduces HelpAgent, an agent-based helpdesk system that can integrate Explainable Artificial Intelligence (XAI) to improve both user experience and operational efficiency. The proposed system utilizes a classifier to automate ticket categorization, offering users the option to either accept the agent’s classification or override it based on personal judgment. A key innovation is the use of Large Language Models (LLMs) to transform complex SHapley Additive exPlanation (SHAP) results into non-expert-friendly narratives through LLMs, ensuring explanations are accessible to both expert and non-expert users. To evaluate system performance, we developed a classification model using multiple Machine Learning (ML) and Deep Learning (DL) architectures, with pre-trained models such as Large Language Model Meta AI (LlaMA) achieving the highest performance. User testing reveals that the majority of participants preferred the proposed system over traditional methods, citing improved usability and trust in the Artificial Intelligence (AI)-driven processes. This work demonstrates the potential of agent-based systems to streamline support workflows while enhancing user satisfaction through explainability and interaction flexibility.

Many central banks are researching and piloting digital versions of fiat money, specifically retail central bank digital currencies (CBDCs). Core to many discussions revolving around these systems’ design is the ability to perform transactions even without network connectivity. While this approach is generally believed to provide additional degrees of freedom for user privacy, the lack of direct involvement of third parties in these offline transfers also interferes with key regulatory requirements that need to be accommodated in the financial space. This paper presents a compliance-by-design approach to evaluate technologies that can balance privacy with anti-money laundering and counter-terrorism financing (AML/CFT) measures. It classifies privacy design options and corresponding technical building blocks for offline CBDCs, along with their impact on AML/CFT measures, and outlines commonalities and differences between offline and online solutions. As such, it provides a conceptual framework for further techno-legal assessments and implementations.

Many central banks are researching and piloting digital versions of fiat money, specifically retail Central Bank Digital Currencies (CBDCs). Core to these systems’ design is the ability to perform transactions even without network connectivity. Due to the lack of direct involvement of third parties in these offline transfers, various regulatory requirements that are key in the financial space need to be accommodated. This paper deploys a compliance-by-design approach to evaluate technologies that can balance privacy with anti-money laundering and counterterrorism financing (AML/CFT) measures. It classifies privacy design options and corresponding technical building blocks for offline CBDCs, along with their impact on AML/CFT measures, and outlines commonalities and differences between offline and online solutions. As such, it provides a conceptual framework for further techno-legal assessments and implementations.

The digital transformation of the medical sector requires solutions that are convenient and efficient for all stakeholders while protecting patients’ sensitive data. One example that has already attracted design-oriented research are medical prescriptions. However, current implementations of electronic prescription management systems typically create centralized data silos, leaving user data vulnerable to cybersecurity incidents and impeding interoperability. Research has also proposed decentralized solutions based on blockchain technology, but privacy-related challenges have often been ignored. We conduct design science research to develop and implement a system for the exchange of electronic prescriptions that builds on two blockchains and a digital wallet app. Our solution combines the bilateral, verifiable, and privacy-focused exchange of information between doctors, patients, and pharmacies through verifiable credentials with a token-based, anonymized double-spending check. Our qualitative and quantitative evaluations as well as a security analysis suggest that this architecture can improve existing approaches to electronic prescription management by offering patients control over their data by design, a high level of security, sufficient performance and scalability, and interoperability with emerging digital identity management solutions for users, businesses, and institutions. We also derive principles on how to design decentralized, privacy-oriented information systems that require both the exchange of sensitive information and double-usage protection.

Analyses that fulfill differential privacy provide plausible deniability to individuals while allowing analysts to extract insights from data. However, beyond an often acceptable accuracy tradeoff, these statistical disclosure techniques generally inhibit the verifiability of the provided information, as one cannot check the correctness of the participants’ truthful information, the differentially private mechanism, or the unbiased random number generation. While related work has already discussed this opportunity, an efficient implementation with a precise bound on errors and corresponding proofs of the differential privacy property is so far missing. In this paper, we follow an approach based on zero-knowledge proofs (ZKPs), in specific succinct non-interactive arguments of knowledge, as a verifiable computation technique to prove the correctness of a differentially private query output. In particular, we ensure the guarantees of differential privacy hold despite the limitations of ZKPs that operate on finite fields and have limited branching capabilities. We demonstrate that our approach has practical performance and discuss how practitioners could employ our primitives to verifiably query individuals’ age from their digitally signed ID card in a differentially private manner.