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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: without files , Cost-effective processing of IoT data in the computing continuumProcessing real-time data in the cloud has become the basis for various IoT applications. This processing typically incurs a high pricing cost, due to the use of paid messaging services (e.g., AWS SQS, or GCP Pub/Sub) that send real-time data from IoT devices at the edge to IoT applications in the cloud. Alternatively, costly AI-enabled IoT devices are employed for federated learning at the edge, which lowers the amount of data sent to the cloud. To reduce such costs, we propose an approach that sends real-time data from the IoT device to the cloud with reduced sampling frequency. In the cloud, we use AI to predict the missing data, and send both real and predicted data to the IoT application. This way, the IoT application receives the same amount of data, although less data is sent from the edge to the cloud (using messaging services). In addition, we measure the prediction error in the cloud, and adjust the sampling frequency at the edge, so that the error remains low. Thus, this approach lowers the cost of IoT applications by reducing the use of paid messaging services, and avoiding costly AI-enabled IoT devices, while also ensuring a low prediction error. For the evaluation, we build a prototype, and we experiment with real-world IoT data. Our experiments show promising results, including a reduction in costs by about 70% with a prediction error of around 5%.Publicationtype: Conference Paper17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Atlas synchronization in the Hierarchical Federated Learning continuumIn Hierarchical Federated Learning (HFL), bandwidth budgets and delay constraints can prevent the timely synchronization of the semantic atlases (summaries) required for clients to learn unseen classes. Conventional synchronization policies typically optimize for geometric freshness. However, staleness becomes most harmful when it changes discrete curriculum gating decisions. We find that reducing feature space drift alone is an unreliable proxy for knowledge void recovery under constraints, because learning depends on decision consistency. Our Decision-Critical Atlas Synchronization (DCAS) policy prioritizes updates that are likely to alter these decisions and accelerates recovery from knowledge voids compared to a geometry-centric baseline.Publicationtype: Conference Paper13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , ContinuumConductor: Decentralized process mining on the edge-cloud continuum(2025-12-08); ; ; ; Process mining traditionally assumes centralized event data collection and analysis. However, modern Industrial Internet of Things systems increasingly operate over distributed, resource-constrained edge-cloud infrastructures. This paper proposes a structured approach for decentralizing process mining by enabling event data to be mined directly within the IoT systems edge-cloud continuum. We introduce ContinuumConductor a layered decision framework that guides when to perform process mining tasks such as preprocessing, correlation, and discovery centrally or decentrally. Thus, enabling privacy, responsive and resource-efficient process mining. For each step in the process mining pipeline, we analyze the trade-offs of decentralization versus centralization across these layers and propose decision criteria. We demonstrate ContinuumConductor at a real-world use-case of process optimazition in inland ports. Our contributions lay the foundation for computing-aware process mining in cyber-physical and IIoT systems.Publicationtype: Preprint33 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Routing cross-blockchain communicationBlockchain Interoperability Solutions (BISs) enable seamless communication and transactions between two or more blockchains. These solutions form an additional heterogeneous set on top of the blockchain layer and impose new constraints on cross-blockchain communication. This new interconnected network of blockchains can provide different communication routes, resulting in the need to find an optimal route between the source and target blockchains. In this paper, we aim to foster discussion about the problems and opportunities that the widescale adoption of BISs brings. We define the problem of using multiple BISs to route cross-blockchain communication and develop theoretical foundations for cross-blockchain routing. Based on the formalization, we implement and evaluate an algorithm that returns an optimal route for a communication request, given a network of blockchains and user-defined objectives.Publicationtype: Conference Paper42 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Simplifying distributed application deployment at the edge through software-defined overlay networks(Elsevier, 2025-10-01); ; ; ; The need for low latency, bandwidth efficiency, and privacy has driven the deployment of distributed applications to the network edge. However, edge environments introduce concrete challenges such as limited infrastructure control, constrained connectivity due to NAT or firewalls, and the heterogeneity of devices and network conditions. This paper introduces a software-defined overlay networking (SDON) middleware that addresses these issues by simplifying the development and deployment of edge applications through centralized control and dynamic overlay management. SDON allows applications to define high-level requirements, such as node and link characteristics and the network topology. These requirements are translated into device-specific configurations and enforced across suitable edge devices. We implemented our SDON middleware as a fully functional software and evaluated it in two edge computing use cases: i) routing for video streaming across middleboxed edge devices and ii) computation offloading on heterogeneous edge devices. Our results show that deployments via SDON, with centrally enforced optimizations, improve application performance by reducing mean streaming latency by 20 % and computation times by 22 %.Publicationtype: Journal Article14 15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Securing blockchain-based distributed learning for heterogeneous clients through knowledge distillation and zero-knowledge proofsDistributed learning (DL) is gaining popularity as it enables clients (e.g., AI Agents) to enhance their machine learning (ML) models’ performance by exchanging knowledge without revealing private datasets. State-of-the-art DL approaches primarily focus on transferring knowledge between heterogeneous clients with diverse model architectures, connecting clients with those that can improve their models, and protecting data privacy. However, they overlook the threat of malicious clients that potentially downgrade the models’ performance by sharing inaccurate knowledge or excluding high-performing clients from the training procedure.Therefore, we introduce the Zero-Knowledge Blockchain-Based Knowledge Distillation Learning Framework (zkBKD). In zkBKD, heterogeneous clients communicate with a blockchain network to discover high-performing clients, verify zero-knowledge proofs (ZKPs) to ensure the correctness of the knowledge shared from other clients, and vote to eliminate malicious clients. We analyze security and privacy risks and show that zkBKD prevents membership, poisoning, and collusion attacks. We conduct extensive experiments on two standard datasets across heterogeneous clients with four model architectures. The experimental results demonstrate that zkBKD relatively improves the average model accuracy of all clients by 25.71%. Even lightweight models such as ResNet-2 achieve up to a 103.35% accuracy gain compared to independent training.Publicationtype: Conference Paper53 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , An AI-powered auto-completion tool for Solidity smart contractsSolidity smart contracts are widely used to implement decentralized applications. However, their development remains challenging due to the language’s domain-specific complexity, the immutability of deployed contracts, which prevents post-deployment fixes, and the high risk of introducing security-critical vulnerabilities. While Large Language Models (LLMs) have advanced code generation across general domains, they often struggle to meet the structural and security-specific demands of smart contract development. Therefore, this paper presents a domain-adapted code completion model trained on 22,000 labeled code constructs extracted from Solidity contracts. The model is built on a transformer-based architecture and fine-tuned using Quantized Low-Rank Adaptation (QLoRA), a parameter-efficient method. The dataset is processed to highlight secure coding patterns and structural semantics, enabling the model to learn from both preceding and succeeding contexts. Evaluation using perplexity, the Bilingual Evaluation Understudy (BLEU) score, and the Metric for Evaluation of Translation with Explicit Ordering (METEOR) shows significant improvements with consistent gains across all three metrics compared to the base model. These results demonstrate that targeted adaptation of language models can significantly enhance coding support in Solidity smart contracts.Publicationtype: Conference Paper36 - 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