Eduardo Brito

Eduardo Brito, Liina Kamm

European digital sovereignty and security increasingly depends 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: not merely what a device or service reports as location, but whether there is contestable evidence about where and when an asserted event occurred, who or what produced the assertion, and under which audit and retention guarantees. There are observable patterns 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.

Fernando Castillo, Eduardo Brito, Pille Pullonen-Raudvere et al.

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.

Eduardo Brito, Fernando Castillo, Amnir Hadachi et al.

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.

Julia Rosenzweig, Eduardo Brito, Hans-Ulrich Kobialka et al.

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.

Eduardo Brito, Max Lübbering, David Biesner et al.

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.

Christian Bauckhage, Eduardo Brito, Kostadin Cvejoski et al.

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.