arXiv subjects

Eduardo Brito

Publications and source records attributed to Eduardo Brito.

14 recordsLinked to original sources

arXiv2026

Enabling Reuse for Data-Sharing Pipelines in Federated Environments

Data mesh architectures enable decentralized data sharing through domain-owned data products, but supporting diverse consumers in federated settings often requires customized data-sharing pipelines. As the number of consumers grows, this leads to a proliferation of pipelines, increasing design and maintenance complexity. We observe that such pipelines frequently exhibit substantial structural overlap. In this paper, we argue that reuse should serve as a guiding principle to address this challenge. We define reuse in data-sharing pipelines as the systematic use of existing data assets and transformation logic across pipelines, and identify reuse opportunities at both design time and runtime. We analyze the associated challenges and outline a reuse-oriented design approach, supported by a reference architecture. A preliminary evaluation demonstrates the potential of reuse to reduce redundancy and improve manageability, providing a pathway toward more scalable and sustainable federated data sharing.

cs.DC↗

arXiv2026

Where Trust Fails: Mapping Location-Data Provenance Risks in Europe

European digital sovereignty and security increasingly depend on whether high-impact decisions can be grounded in location evidence that remains credible under adversarial pressure. This paper frames a cross-sector analysis as a location-data provenance problem: whether there is contestable evidence about where and when an asserted event occurred, who or what produced the assertion, what a device or service reported as location, and which audit and retention guarantees apply. Trust-erosion patterns are observable across democratic processes and the information environment, trade and origin-sensitive supply chains, finance and illicit shipping flows, critical infrastructure and mobility, and harms targeting individuals' private and social domains. In these patterns we see a recurring asymmetry in which locality, presence, routing, or jurisdiction can be asserted cheaply while institutions and affected parties face costly reconstruction when disputes arise. To make this challenge actionable, this paper introduces a compact risk taxonomy that decomposes provenance failures into integrity axes and recurring failure modes, and derives design expectations for next-generation digital trust infrastructure centered on contestability under dispute, while remaining privacy- and rights-compatible. It argues for treating location as a digital primitive that should be represented as evidence-bearing claims rather than self-asserted coordinates, and positions proof-of-location (PoL) mechanisms as a candidate capability layer for producing verifiable presence claims under explicit threat and privacy assumptions. The outcome is a sector-neutral foundation for future architectural work on a next-generation digital trust infrastructure for Europe.

cs.CR↗

arXiv2026

Toward Practical Decentralized Proof-of-Location via Physical Witnessing Zones

Digital services increasingly rely on claims that a person, device, or asset was in a specific place at a specific time. Today, those claims often depend on self-reported location data, which is easy to falsify and difficult to verify after the fact. Proof-of-Location (PoL) systems address this gap by turning presence claims into evidence that an independent verifier can later inspect. This paper builds upon recent theoretical work on decentralized PoL architectures and demonstrates how they can move from emulation to a physical prototype built with low-cost hardware. We implement a witnessing zone in which fixed nearby devices measure a prover's presence, exchange claims over a local mesh, and record them in a tamper-evident ledger. Building the prototype required adapting the abstract protocol to physical constraints through witness-initiated ranging, cross-witness consistency checks, and freshness binding against replay. Our controlled indoor evaluation shows that the system can produce accurate, low-latency proof objects while detecting simulated replay and malicious-ranging attacks. The result is a reusable experimental baseline for next-generation digital trust infrastructure, that exposes the remaining calibration, verifier-independence, radio-integrity, and scaling requirements for decentralized location evidence, where physical presence claims can be independently checked under real radio, networking, and timing conditions.

cs.CR↗

arXiv2026

An Evidence-driven Protocol for Trustworthy CI Pipelines

Enterprise software supply chains are increasingly vulnerable to infrastructure attacks, resulting in financial and reputational damage. Ensuring the integrity and provenance of software artifacts remains a significant challenge, where re-execution of the build and tests by every consumer to guarantee provenance produces a verification bottleneck and credibility reduction. This paper presents an evidence-driven protocol for trustworthy Continuous Integration (CI) pipelines that combines Deterministic Build Systems (DBS) with Trusted Execution Environments (TEEs). The approach provides cryptographically verifiable guarantees of integrity, authenticity, and attestation for CI artifacts in distributed environments, reducing implicit trust without requiring costly re-execution by consumers. We introduce a protocol that binds deterministic builds with TEE-based attestations, formalizing the evidence life cycle, together with a practical implementation using Nix and Intel TDX. Experimental results show that artifact verification is reduced from redundant computation to lightweight signature and policy checks. These findings demonstrate that evidence-driven CI pipelines establish scalable and verifiable trust in digital infrastructure, effectively amortizing the initial computational overhead introduced by TEEs.

cs.CR↗

arXiv2026

Decentralized Proof-of-Location for Content Provenance: Towards Capture-Time Authenticity

Reliable use of real-world data requires confidence that recorded evidence reflects what actually occurred at the moment of capture. In adversarial or incentive-misaligned cyber-physical settings, device-centric provenance and post-capture verification are insufficient to provide that guarantee. This paper builds on Proof-of-Location (PoL) as a baseline for establishing where and when events take place, and extends it with a witnessing-zone architecture in which multiple independent observers collectively validate physical events. The resulting approach produces auditable evidence artifacts that can support downstream systems in cyber-physical settings, without relying on centralized trust. Through representative scenarios and simulation-based evaluation, this paper shows how such architectures improve sensor data trustworthiness and resilience to fabricated or staged events.

cs.CR↗

arXiv2025

Towards Trusted Service Monitoring: Verifiable Service Level Agreements

Service Level Agreement (SLA) monitoring in service-oriented environments suffers from inherent trust conflicts when providers self-report metrics, creating incentives to underreport violations. We introduce a framework for generating verifiable SLA violation claims through trusted hardware monitors and zero-knowledge proofs, establishing cryptographic foundations for genuine trustworthiness in service ecosystems. Our approach starts with machine-readable SLA clauses converted into verifiable predicates and monitored within Trusted Execution Environments. These monitors collect timestamped telemetry, organize measurements into Merkle trees, and produce signed attestations. Zero-knowledge proofs aggregate Service-Level Indicators to evaluate compliance, generating cryptographic proofs verifiable by stakeholders, arbitrators, or insurers in disputes, without accessing underlying data. This ensures three security properties: integrity, authenticity, and validity. Our prototype demonstrates linear scaling up to over 1 million events per hour for measurements with near constant-time proof generation and verification for single violation claims, enabling trustless SLA enforcement through cryptographic guarantees for automated compliance verification in service monitoring.

cs.CR↗

arXiv2025

A Taxonomy and Methodology for Proof-of-Location Systems

Digital societies increasingly rely on trustworthy proofs of physical presence for services such as supply-chain tracking, e-voting, ride-sharing, and location-based rewards. Yet, traditional localization methods often lack cryptographic guarantees of where and when an entity was present, leaving them vulnerable to spoofing, replay, or collusion attacks. In response, research on Proof-of-Location (PoL) has emerged, with recent approaches combining distance bounding, distributed consensus, and privacy-enhancing techniques to enable verifiable, tamper-resistant location claims. As the design space for PoL systems grows in complexity, this paper provides a unified framework to help practitioners navigate diverse application needs. We first propose a taxonomy identifying four core domains: (1) cryptographic guarantees, (2) spatio-temporal synchronization, (3) trust and witness models, and (4) interaction and overhead. Building on this, we introduce a methodology to map application-specific requirements onto appropriate PoL architectures. We illustrate this process through three use cases (retail e-coupons, supply chain auditing, and physical e-voting), each showing how different constraints shape protocol choices. Overall, this work offers a structured approach to building secure, scalable, and interoperable PoL systems.

cs.CR↗

arXiv2025

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework

The growing reliance on data-driven applications in sectors such as healthcare, finance, and law enforcement underscores the need for secure, privacy-preserving, and scalable mechanisms for data generation and sharing. Synthetic data generation (SDG) has emerged as a promising approach but often relies on centralized or external processing, raising concerns about data sovereignty, domain ownership, and compliance with evolving regulatory standards. To overcome these issues, we introduce SynthGuard, a framework designed to ensure computational governance by enabling data owners to maintain control over SDG workflows. SynthGuard supports modular and privacy-preserving workflows, ensuring secure, auditable, and reproducible execution across diverse environments. In this paper, we demonstrate how SynthGuard addresses the complexities at the intersection of domain-specific needs and scalable SDG by aligning with requirements for data sovereignty and regulatory compliance. Developed iteratively with domain expert input, SynthGuard has been validated through real-world use cases, demonstrating its ability to balance security, privacy, and scalability while ensuring compliance. The evaluation confirms its effectiveness in implementing and executing SDG workflows and integrating privacy and utility assessments across various computational environments.

cs.CR↗

arXiv2025

Zero-Knowledge Proof-of-Location Protocols for Vehicle Subsidies and Taxation Compliance

This paper introduces a new set of privacy-preserving mechanisms for verifying compliance with location-based policies for vehicle taxation, or for (electric) vehicle (EV) subsidies, using Zero-Knowledge Proofs (ZKPs). We present the design and evaluation of a Zero-Knowledge Proof-of-Location (ZK-PoL) system that ensures a vehicle's adherence to territorial driving requirements without disclosing specific location data, hence maintaining user privacy. Our findings suggest a promising approach to apply ZK-PoL protocols in large-scale governmental subsidy or taxation programs.

cs.CR↗

arXiv2025

Trustworthy Decentralized Autonomous Machines: A New Paradigm in Automation Economy

Decentralized Autonomous Machines (DAMs) represent a transformative paradigm in automation economy, integrating artificial intelligence (AI), blockchain technology, and Internet of Things (IoT) devices to create self-governing economic agents participating in Decentralized Physical Infrastructure Networks (DePIN). Capable of managing both digital and physical assets and unlike traditional Decentralized Autonomous Organizations (DAOs), DAMs extend autonomy into the physical world, enabling trustless systems for Real and Digital World Assets (RDWAs). In this paper, we explore the technological foundations, and challenges of DAMs and argue that DAMs are pivotal in transitioning from trust-based to trustless economic models, offering scalable, transparent, and equitable solutions for asset management. The integration of AI-driven decision-making, IoT-enabled operational autonomy, and blockchain-based governance allows DAMs to decentralize ownership, optimize resource allocation, and democratize access to economic opportunities. Therefore, in this research, we highlight the potential of DAMs to address inefficiencies in centralized systems, reduce wealth disparities, and foster a post-labor economy.

cs.MA↗

arXiv2024

TrustOps: Continuously Building Trustworthy Software

Software services play a crucial role in daily life, with automated actions determining access to resources and information. Trusting service providers to perform these actions fairly and accurately is essential, yet challenging for users to verify. Even with publicly available codebases, the rapid pace of development and the complexity of modern deployments hinder the understanding and evaluation of service actions, including for experts. Hence, current trust models rely heavily on the assumption that service providers follow best practices and adhere to laws and regulations, which is increasingly impractical and risky, leading to undetected flaws and data leaks. In this paper, we argue that gathering verifiable evidence during software development and operations is needed for creating a new trust model. Therefore, we present TrustOps, an approach for continuously collecting verifiable evidence in all phases of the software life cycle, relying on and combining already existing tools and trust-enhancing technologies to do so. For this, we introduce the adaptable core principles of TrustOps and provide a roadmap for future research and development.

cs.SE↗

arXiv2021

Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis

Many machine learning applications can benefit from simulated data for systematic validation - in particular if real-life data is difficult to obtain or annotate. However, since simulations are prone to domain shift w.r.t. real-life data, it is crucial to verify the transferability of the obtained results. We propose a novel framework consisting of a generative label-to-image synthesis model together with different transferability measures to inspect to what extent we can transfer testing results of semantic segmentation models from synthetic data to equivalent real-life data. With slight modifications, our approach is extendable to, e.g., general multi-class classification tasks. Grounded on the transferability analysis, our approach additionally allows for extensive testing by incorporating controlled simulations. We validate our approach empirically on a semantic segmentation task on driving scenes. Transferability is tested using correlation analysis of IoU and a learned discriminator. Although the latter can distinguish between real-life and synthetic tests, in the former we observe surprisingly strong correlations of 0.7 for both cars and pedestrians.

cs.CV↗

arXiv2019

Towards Supervised Extractive Text Summarization via RNN-based Sequence Classification

This article briefly explains our submitted approach to the DocEng'19 competition on extractive summarization. We implemented a recurrent neural network based model that learns to classify whether an article's sentence belongs to the corresponding extractive summary or not. We bypass the lack of large annotated news corpora for extractive summarization by generating extractive summaries from abstractive ones, which are available from the CNN corpus.

cs.CL↗

arXiv2017

Adiabatic Quantum Computing for Binary Clustering

Quantum computing for machine learning attracts increasing attention and recent technological developments suggest that especially adiabatic quantum computing may soon be of practical interest. In this paper, we therefore consider this paradigm and discuss how to adopt it to the problem of binary clustering. Numerical simulations demonstrate the feasibility of our approach and illustrate how systems of qubits adiabatically evolve towards a solution.

stat.ML↗