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Item type:Publication, Access status: with files , zkPACT: a zero-knowledge private cross-chain token transfer framework utilizing decentralized oracle networks(Elsevier, 2026-06-11); ; ; ; Tirado, Juan M.Despite the growing adoption of blockchains, their isolated architectures hinder seamless cross-chain communication, challenging applications that rely on integrated blockchain infrastructures, notably Blockchain-based Information Systems (BISs). Achieving interoperability while preserving privacy and regulatory compliance remains a core challenge, particularly when separate organizations operate different blockchain platforms and tokenized value must move across them without exposing transaction links that may reveal business relationships or payment behavior. Existing interoperability solutions often incur high computational overhead and rely on protocol-specific assumptions, limiting their applicability across heterogeneous blockchains. We introduce zkPACT, a privacy-preserving framework for compliant cross-chain token transfers across heterogeneous blockchains. Our framework combines Zero-Knowledge Proofs (ZKPs), oracle networks, and off-chain batching to support scalable transfers. It employs a coordinated oracle model in which validators process cross-chain burn events, while a rotating aggregator updates the shared off-chain Merkle tree after reaching consensus, enabling private and efficient token claims. To improve scalability and reduce gas costs, zkPACT batches claim requests off-chain and then submits a single succinct proof to the smart contract. To ensure validator accountability, the framework enforces an incentive mechanism and dynamic slashing. We also integrate a Know Your Customer (KYC) mechanism that enables users to demonstrate compliance without revealing sensitive data, preserving privacy and accountability in the event of abuse. We present a proof-of-concept implementation of zkPACT that achieves up to 95% lower gas costs and up to 94% lower off-chain memory usage than a non-batching approach, demonstrating its suitability for private, scalable cross-chain token transfers.Publicationtype: Journal Article18 17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Opinion dynamics with median aggregation(Elsevier, 2026-03-30); ; ; ; Understanding the formation and evolution of opinions is of broad interdisciplinary interest. Many classical models for opinion formation focus on the impact of different notions of locality, e.g., locality due to network effects among agents or the role of the proximity of opinions. In practice, however, opinion formation is often governed by the interplay of local and global influences. In this paper, we study these influences with a model for opinion formation of agents embedded in a social network. Each agent has a static intrinsic opinion as well as a public opinion that is updated asynchronously over time. Moreover, agents have access to a global aggregate (e.g., the outcome of a vote) of all public opinions. We focus on the popular median voting rule and show that pure Nash equilibria always exist. For every initial state of the dynamics, a pure equilibrium can be reached. The set of reachable equilibria forms a complete lattice, and extremal equilibria can be computed in polynomial time. We show that by uniformly increasing the influence of the global median we can enforce that the median opinion is the same in every reachable equilibrium. We can compute the increase scheme that achieves this property in polynomial time. In contrast, when we can increase the influence of the global median for a set of at most k agents, finding the set that leads to a unique median opinion in every reachable equilibrium is NP-complete.Publicationtype: Journal Article14 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , (Almost) perfect discrete iterative load balancing(Society for Industrial & Applied Mathematics (SIAM), 2026-01); ; ; ; We consider discrete, iterative load balancing via matchings on arbitrary graphs. Initially each node holds a certain number of tokens, defining the load of the node, and the objective is to redistribute the tokens such that eventually each node has approximately the same number of tokens. We present results for a general class of simple local balancing schemes where the tokens are balanced via matchings. In each round the process averages the tokens of any two matched nodes. If the sum of their tokens is odd, the node to receive the one excess token is selected at random. Our class covers three popular models: in the matching model a new matching is generated randomly in each round, in the balancing circuit model a fixed sequence of matchings is applied periodically, and in the asynchronous model the load is balanced over a randomly chosen edge. We measure the quality of a load vector by its discrepancy, defined as the difference between the maximum and minimum load across all nodes. As our main result we show that with high probability our discrete balancing scheme reaches a discrepancy of 3 in a number of rounds which asymptotically matches the spectral bound for continuous load balancing with fractional load. This result improves and tightens a long line of previous works, by not only achieving a small constant discrepancy (instead of a non-explicit, large constant) but also holding for arbitrary instead of regular graphs. The result also demonstrates that in the general model we consider, discrete load balancing is no harder than continuous load balancing.Publicationtype: Conference Paper15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , FLUID: federated learning with unlearning and instant drift-recoveryFederated Learning (FL) enables collaborative model training without sharing raw data, but struggles with concept drift, where client data distributions evolve over time. Existing approaches either retain outdated knowledge or suffer from high computational costs due to frequent retraining. This work introduces Federated Learning with Unlearning and Instant Driftrecovery (FLUID), a novel framework that treats concept drift as a selective forgetting problem. By leveraging techniques from Federated Unlearning, FLUID removes obsolete knowledge while adapting to new data patterns. It combines the lightweight unlearning capability of Federated Auxiliary Unlearning (FedAU) with the rapid recovery capability of Rapid Retraining (RRT), without requiring full retraining. Experimental results show that FLUID achieves balanced, all-around performance matching RRT in recovery speed and better convergence, and FedAU in lower accuracy dips during drift, and it outperforms natural recovery capabilities of Federated Averaging (FedAvg), and retraining from scratch.Publicationtype: Conference Paper81 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , UPPR: universal privacy-preserving revocation(IEEE, 2025-10); ; ; ; Self-Sovereign Identity (SSI) frameworks enable individuals to receive and present digital credentials in a user-controlled way. Revocation mechanisms ensure that invalid or withdrawn credentials cannot be misused. These revocation mechanisms must be scalable (e.g., at national scale) and preserve core SSI principles such as privacy, user control, and interoperability. Achieving both is hard, and finding a suitable trade-off remains a key challenge in SSI research.This paper introduces UPPR, a revocation mechanism for One-Show Verifiable Credentials (oVCs) and unlinkable Anonymous Credentials (ACs). Revocations are managed using percredential Verifiable Random Function (VRF) tokens, which are published in a Bloom filter cascade on a blockchain. Holders prove non-revocation via a VRF proof for oVCs or a single Zero-Knowledge Proof for ACs. The construction prevents revocation status tracking, allows holders to stay offline, and hides issuer revocation behavior. We analyze the privacy properties of UPPR and provide a prototype implementation on Ethereum. Our implementation enables off-chain verification at no cost. On-chain checks cost 0.56–0.84 USD, while issuers pay only 0.00002–0.00005 USD per credential to refresh the revocation state.Publicationtype: Conference Paper43 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Detect, adapt, overcome: mitigating concept drift in federated learningFederated Learning (FL) is a distributed machine learning paradigm where a central server coordinates the training of a global model across multiple decentralized clients. Most FL algorithms assume stationary data-generating processes and neglect concept drift, i.e., the change of data distributions over time. This results in a gradual decline of the model's performance and eventually renders the model obsolete. We investigate the impact of concept drift in FL and propose an active approach to detect and adapt to drift. Our detection method is based on local model loss which allows us to categorize clients based on the type of drift they experience and apply appropriate adaptive measures. Through extensive experiments, we demonstrate the efficiency, accuracy, and precision of our approach: the proposed adaptation approach successfully accelerates convergence for drifted clients, outperforming the baseline algorithm. Even under extreme drift conditions, our global model remains stable. This demonstrates the superior robustness of our approach compared to conventional methods.Publicationtype: Conference Paper62 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Enhancing privacy through unlinkable data sharing with user-in-the-loop access controlIn our information-driven society, the volume of data generated by individuals has grown significantly. Protecting the privacy of individuals is becoming more challenging, as this data can reveal detailed insights into personal preferences and behavior. To address this challenge, we introduce a user-centric, privacy-preserving data-sharing solution that leverages a central data storage service, hereafter referred to as the data intermediary. By integrating local differential privacy with user-in-the-Ioop access control, our system enables data providers to securely and unlinkably store their data at the intermediary. Data consumers can localize and request data via the intermediary. The data providers are included in access decisions without disclosing their identity nor by enabling the linking of their data. We evaluated our approach using theoretical analysis and simulations. Our findings indicate that our system achieves c-privacy and safeguards data providers against external and internal attackers, malicious data consumers, and an honest-but-curious intermediary. Moreover, our method reduces the message overhead for data discovery in our system by more than half compared to existing approaches.Publicationtype: Conference Paper12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Hierarchical bidirectional aggregation for federated learning under concept driftFederated Learning (FL) enables collaborative model training across decentralized clients while preserving data privacy. However, real-world FL deployments face two fundamental challenges: heterogeneous data distributions and concept drift, where data distributions evolve over time. While traditional FL assumes a star topology, real-world scenarios often employ hierarchical federated learning based on tree topologies that improve scalability and communication efficiency. In this paper, we introduce Hierarchical Bidirectional Averaging (HBA), a novel aggregation strategy for hierarchical federated networks designed to enhance resilience against data heterogeneity and concept drift. Our main result is that HBA significantly strengthens the resilience of FL against concept drift. In particular, our key findings demonstrate that (1) servers higher in the hierarchy exhibit greater accuracy stability and retain generalized model representations while the lower servers capture more localized and fine-grained details, (2) increasing tree height improves overall client accuracy, and (3) our aggregation strategy enhances generalization and facilitates a balanced information flow across the hierarchy, resulting in faster recovery from concept drift.Publicationtype: Conference Paper40 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Unlinkable data sharing with dynamic access control(Association for Computing Machinery (ACM), 2025-03); ; In an increasingly information-driven society, the volume of digital footprints left by individuals has surged significantly. Safeguarding the anonymity of data generated by computing devices is becoming more challenging as these offer deep insights into personal behaviors. We propose a user-centric and privacy-preserving data space for unlinkable data sharing based on a central intermediary. By integrating differential privacy techniques with fine-grained access control, our system allows data providers to store their data confidentially and unlinkable at the intermediary. Data consumers can then locate and request data via this intermediary, ensuring that data providers remain informed without revealing the origin of the data. Additionally, the intermediary facilitates continuous data sharing, requiring only a single data upload. Our approach is designed to protect data providers from both external and internal attackers, as well as from an honest-but-curious intermediary.Publicationtype: Conference Paper12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Towards WebAssembly-based federated learning(Springer, 2025-02); ; ; WebAssembly is a portable binary instruction format designed to serve as a compilation target for high-level languages. While originally developed to run performance-intensive applications directly in Web browsers, WebAssembly supports these days a number of different hardware platforms across the compute continuum. This makes it a promising option to run services for training and inference in Federated Learning. To the best of our knowledge, there have been only a few practical approaches to realize Federated Learning using WebAssembly. Therefore, in this paper, we present a framework to achieve this. Our prototypical implementation shows that WebAssembly-based Federated Learning applications are highly portable while providing acceptable runtime overhead during model training.Publicationtype: Conference Paper36