Where Trust Fails: Mapping Location-Data Provenance Risks in Europe
- URL: http://arxiv.org/abs/2604.13668v1
- Date: Wed, 15 Apr 2026 09:36:50 GMT
- Title: Where Trust Fails: Mapping Location-Data Provenance Risks in Europe
- Abstract summary: This paper frames a cross-sector analysis as a location-data provenance problem.<n>It argues for treating location as a digital primitive that should be represented as evidence-bearing claims.<n>The outcome is a sector-neutral foundation for future architectural work on a next-generation digital trust infrastructure for Europe.
- Score: 1.0026496861838448
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
Related papers
- AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation [48.55515767840701]
"AI slop" is artifacts, hallucinated vulnerabilities, plausible but incorrect patches, and semantically repackaged bug reports.<n>This paper surveys the empirical evidence, identifies a unifying mechanism, and traces a path toward trustworthy triage.
arXiv Detail & Related papers (2026-08-26T11:48:38Z) - Toward Practical Decentralized Proof-of-Location via Physical Witnessing Zones [2.0415910628419067]
Digital services increasingly rely on claims that a person, device, or asset was in a specific place at a specific time.<n>PoL systems turn presence claims into evidence that an independent verifier can later inspect.<n>This paper builds upon recent theoretical work on decentralized PoL architectures.
arXiv Detail & Related papers (2026-08-05T15:27:42Z) - Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle [65.6642776933861]
This survey examines the protective paradigm that has grown around this intervention point.<n>We find that most protections are still validated mainly against static or weakly adaptive adversaries.<n>We close by consolidating cross-stage countermeasures and open problems for robust, composable, and deployable owner-side protection.
arXiv Detail & Related papers (2026-08-05T00:46:50Z) - Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations [55.76952683833966]
This paper proposes weighted CONCH (W-CONCH) and weighted CROC (W-CROC) to reduce confidence set size when data may be corrupted.<n>Experiments on image-based and real-world changepoint and root-cause benchmarks show that uncertainty-based weighting substantially reduces confidence set size.
arXiv Detail & Related papers (2026-07-29T05:16:59Z) - Modeling Misinformation as a Commons Problem [0.0]
This paper presents an agent-based simulation that frames trust as a collective resource and attention as a scarce private budget.<n>Across experiments, the model produces four recurring modes: credible stability, misinformation dominance, polarization, and a mixed baseline.
arXiv Detail & Related papers (2026-07-08T04:11:48Z) - Authenticity Debt and the Synthetic Content Threat Landscape: A Layered Framework for Trust, Provenance, and IP Governance in the Generative AI Era [2.113921717639734]
Generative artificial intelligence has fundamentally changed how content is produced.<n>This paper presents a comprehensive, multi-dimensional taxonomy of generative AI harms and attack vectors.<n>We argue that no single mechanism is sufficient in open, adversarial, and evolving environments.
arXiv Detail & Related papers (2026-05-30T08:51:55Z) - Decentralized Proof-of-Location for Content Provenance: Towards Capture-Time Authenticity [38.84282329289211]
This paper builds on Proof-of-Location (PoL) as a baseline for establishing where and when events take place.<n>It extends it with a witnessing-zone architecture in which multiple independent observers collectively validate physical events.<n>The resulting approach produces auditable evidence artifacts that can support downstream systems in cyber-physical settings.
arXiv Detail & Related papers (2026-03-29T21:43:48Z) - Distributed Security: From Isolated Properties to Synergistic Trust [5.240957672918797]
This vision paper argues for a fundamental shift in how we approach distributed security.<n>We conclude four foundational properties, textitagreement, consistency, privacy, verifiability, accountability<n>We identify critical research challenges: discovering new security properties driven by emerging applications, managing the computational overhead of cryptographic primitives in high-performance consensus layers, and addressing post-quantum and human-factor challenges.
arXiv Detail & Related papers (2026-02-20T08:31:18Z) - Prediction Laundering: The Illusion of Neutrality, Transparency, and Governance in Polymarket [17.356126657763806]
This paper presents a qualitative sociotechnical audit of Polymarket (N = 27), combining digital ethnography, interpretive walkthroughs, and semi-structured interviews.<n>We show how subjective, high-uncertainty bets, strategic hedges, and capital-heavy whale activity are stripped of their original noise through algorithmic aggregation.
arXiv Detail & Related papers (2026-02-05T01:19:25Z) - Hallucination Detection and Mitigation in Large Language Models [0.0]
Large Language Models (LLMs) and Large Reasoning Models (LRMs) offer transformative potential for high-stakes domains like finance and law.<n>Their tendency to hallucinate, generating factually incorrect or unsupported content, poses a critical reliability risk.<n>This paper introduces a comprehensive framework for hallucination management, built on a continuous improvement cycle driven by root cause awareness.
arXiv Detail & Related papers (2026-01-14T23:19:37Z) - Making LLMs Reliable When It Matters Most: A Five-Layer Architecture for High-Stakes Decisions [51.56484100374058]
Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes.<n>This gap, driven by mutually cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector.<n>This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure.
arXiv Detail & Related papers (2025-11-10T22:24:21Z) - On the Credibility of Deniable Communication in Court [1.8047694351309207]
Evidentiary processes in courts have been developed over centuries to account for the reality that evidence has always been forgeable.<n>We argue that deniability does not and need not change this paradigm.
arXiv Detail & Related papers (2025-10-19T15:14:43Z) - Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention [65.47632669243657]
A dishonest institution can exploit mechanisms to discriminate or unjustly deny services under the guise of uncertainty.<n>We demonstrate the practicality of this threat by introducing an uncertainty-inducing attack called Mirage.<n>We propose Confidential Guardian, a framework that analyzes calibration metrics on a reference dataset to detect artificially suppressed confidence.
arXiv Detail & Related papers (2025-05-29T19:47:50Z) - Benchmarking the Spatial Robustness of DNNs via Natural and Adversarial Localized Corruptions [49.546479320670464]
This paper introduces specialized metrics for benchmarking the spatial robustness of segmentation models.<n>We propose region-aware multi-attack adversarial analysis, a method that enables a deeper understanding of model robustness.<n>The results reveal that models respond to these two types of threats differently.
arXiv Detail & Related papers (2025-04-02T11:37:39Z) - Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges [53.2306792009435]
This paper introduces a novel framework for detecting instability in smart grids using only stable data.<n>It achieves up to 98.1% accuracy in predicting grid stability and 98.9% in detecting adversarial attacks.<n>Implemented on a single-board computer, it enables real-time decision-making with an average response time of under 7ms.
arXiv Detail & Related papers (2025-01-27T20:48:25Z) - From Mean to Extreme: Formal Differential Privacy Bounds on the Success of Real-World Data Reconstruction Attacks [54.25638567385662]
Differential Privacy in machine learning is often interpreted as guarantees against membership inference.<n> translating DP budgets into quantitative protection against the more damaging threat of data reconstruction remains a challenging open problem.<n>This paper bridges the critical gap by deriving the first formal privacy bounds tailored to the mechanics of demonstrated "from-scratch" attacks.
arXiv Detail & Related papers (2024-02-20T09:52:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.