arXiv subjects

Liina Kamm

Publications and source records attributed to Liina Kamm.

3 recordsLinked to original sources

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↗

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↗