A Taxonomy and Methodology for Proof-of-Location Systems
- URL: http://arxiv.org/abs/2508.14230v1
- Date: Tue, 19 Aug 2025 19:39:05 GMT
- Title: A Taxonomy and Methodology for Proof-of-Location Systems
- Abstract summary: We propose a taxonomy identifying four core domains of Proof-of-Location (PoL) systems.<n>We then introduce a methodology to map application-specific requirements onto appropriate PoL architectures.<n>Overall, this work offers a structured approach to building secure, scalable, and interoperable PoL systems.
- Score: 42.222053626544366
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
Related papers
- Achieving Text-based Person Retrieval with Any Granularity [80.69427598925718]
This paper introduces a new paradigm, Text-based Person Retrieval with Any Granularity.<n>We formalize a five-level granularity spectrum and construct UFine6926-MG, a high-quality multi-grained annotated dataset.<n>We then propose the Cross-modal Multi-grained Aligning and Matching framework.
arXiv Detail & Related papers (2026-07-23T08:42:25Z) - A Verifier-Centric Conceptual Model for Digital Credential Ecosystems [0.0]
We present a verifier-centric conceptual model built on two decompositions.<n>We show that it explains interoperability failures, verifier-side burden, offline verifiability, privacy implications, and terminological ambiguities that element-wise comparison leaves unresolved.
arXiv Detail & Related papers (2026-07-12T12:59:26Z) - A Technical Taxonomy of LLM Agent Communication Protocols [60.76747983053368]
This study develops a technical taxonomy to classify and analyze large language models (LLMs) agent communication protocols.<n>The framework guides protocol selection and highlights open research gaps such as privacy and policy enforcement.
arXiv Detail & Related papers (2026-06-17T14:45:20Z) - Citation-Closure Retrieval and Per-Rule Attribution for Real-World Regulatory Compliance Question Answering [54.97384993676565]
We formalize Regulatory Compliance QA with RegOps-Bench, a novel benchmark featuring an Operational Knowledge Graph derived from complex national R&D regulations.<n>RefWalk traverses cross-document citations, fuses multi-view candidates via max-based aggregation, and enforces per-rule attribution to explicitly map claims to sources.
arXiv Detail & Related papers (2026-05-28T10:38:38Z) - Context-Binding Gaps in Stateful Zero-Knowledge Proximity Proofs: Taxonomy, Separation, and Mitigation [0.0]
A zero proximity proof certifies geometric nearness but carries no commitment to an application context.<n>We present a systems-security analysis of this deployment problem.<n>We show that sameepoch transfer is realistic in dense urban deployments unless per-request nonces are maintained.
arXiv Detail & Related papers (2026-04-05T00:10:25Z) - SoK: Practical Aspects of Releasing Differentially Private Graphs [20.29082580927169]
Graph data is increasingly prevalent across domains, offering analytical value but raising privacy concerns.<n>Applying Differential Privacy to graphs is challenging because of their complex relational structure.<n>Existing methods vary in privacy definitions, utility goals, and contextual settings, complicating comparison.<n>We propose a novel systemisation of existing methods tailored to practical considerations and adaptable to varying practitioner objectives.
arXiv Detail & Related papers (2026-03-19T11:30:59Z) - Logics-Parsing-Omni Technical Report [18.897248420641386]
This paper proposes the Omni Parsing framework to address the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing.<n>A pivotal advantage of this framework is its evidence anchoring mechanism, which enforces a strict alignment between high-level semantic descriptions and low-level facts.<n>This enables evidence-based'' logical induction, transforming unstructured signals into standardized knowledge that is locatable, enumerable, and traceable.
arXiv Detail & Related papers (2026-03-10T13:46:32Z) - GenAI for Systems: Recurring Challenges and Design Principles from Software to Silicon [62.2138479061386]
Generative AI is reshaping how computing systems are designed, optimized, and built, yet research remains fragmented across software, architecture, and chip design communities.<n>This paper takes a cross-stack perspective, examining how generative models are being applied from code generation and distributed runtimes through hardware design space exploration to RTL synthesis, physical layout, and verification.
arXiv Detail & Related papers (2026-02-16T22:45:33Z) - Why Does the LLM Stop Computing: An Empirical Study of User-Reported Failures in Open-Source LLMs [50.075587392477935]
We conduct the first large-scale empirical study of 705 real-world failures from the open-source DeepSeek, Llama, and Qwen ecosystems.<n>Our analysis reveals a paradigm shift: white-box orchestration relocates the reliability bottleneck from model algorithmic defects to the systemic fragility of the deployment stack.
arXiv Detail & Related papers (2026-01-20T06:42:56Z) - LibContinual: A Comprehensive Library towards Realistic Continual Learning [62.34449396069085]
A fundamental challenge in Continual Learning (CL) is catastrophic forgetting, where adapting to new tasks degrades the performance on previous ones.<n>We propose LibContinual, a comprehensive and reproducible library designed to serve as a foundational platform for realistic CL.
arXiv Detail & Related papers (2025-12-26T13:59:13Z) - LLM-Aligned Geographic Item Tokenization for Local-Life Recommendation [21.430438767288106]
LGSID is an LLM-Hierarchical Geographic Item Tokenization Framework for Local-life Recommendation.<n>We introduce a novel G-DPO algorithm that uses pre-trained reward model to inject generalized spatial knowledge and collaborative signals into LLMs.<n>Experiments on real-world Kuaishou industry datasets show that LGSID consistently outperforms state-of-the-art discriminative and generative recommendation models.
arXiv Detail & Related papers (2025-11-18T07:54:32Z) - RecGPT: A Foundation Model for Sequential Recommendation [16.464972558861497]
We develop a foundation model for sequential recommendation that achieves genuine zero-shot generalization capabilities.<n>Our approach departs from existing ID-based methods by deriving item representations exclusively from textual features.<n>We introduce unified item tokenization with Finite Scalar Quantization that transforms heterogeneous textual descriptions into standardized discrete tokens.
arXiv Detail & Related papers (2025-06-06T17:53:02Z) - Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning [58.57194301645823]
Large language models (LLMs) are increasingly integrated into real-world personalized applications.<n>The valuable and often proprietary nature of the knowledge bases used in RAG introduces the risk of unauthorized usage by adversaries.<n>Existing methods that can be generalized as watermarking techniques to protect these knowledge bases typically involve poisoning or backdoor attacks.<n>We propose name for harmless' copyright protection of knowledge bases.
arXiv Detail & Related papers (2025-02-10T09:15:56Z) - Object-Centric Conformance Alignments with Synchronization (Extended Version) [57.76661079749309]
We present a new formalism that combines the ability of object-centric Petri nets to capture one-to-many relations and the one of Petri nets with identifiers to compare and synchronize objects based on their identity.
We propose a conformance checking approach for such nets based on an encoding in satisfiability modulo theories (SMT)
arXiv Detail & Related papers (2023-12-13T21:53:32Z) - A Verification Framework for Component-Based Modeling and Simulation
Putting the pieces together [0.0]
The proposed verification framework provides methods, techniques and tool support for verifying composability at its different levels.
In particular we focus on the Dynamic-Semantic Composability level due to its significance in the overall composability correctness and also due to the level of difficulty it poses in the process.
arXiv Detail & Related papers (2023-01-08T18:53:28Z) - Learning Classifiers of Prototypes and Reciprocal Points for Universal
Domain Adaptation [79.62038105814658]
Universal Domain aims to transfer the knowledge between datasets by handling two shifts: domain-shift and categoryshift.
Main challenge is correctly distinguishing the unknown target samples while adapting the distribution of known class knowledge from source to target.
Most existing methods approach this problem by first training the target adapted known and then relying on the single threshold to distinguish unknown target samples.
arXiv Detail & Related papers (2022-12-16T09:01:57Z) - Vertical Federated Linear Contextual Bandits [36.40993993623568]
We design a customized encryption scheme named matrix-based mask mechanism(O3M) for encrypting local contextual information.
We apply the mechanism to two commonly-used bandit algorithms, LinUCB and LinTS, and instantiate two practical protocols for online recommendation.
arXiv Detail & Related papers (2022-10-20T06:59:42Z) - Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive
Privacy Analysis and Beyond [57.10914865054868]
We consider vertical logistic regression (VLR) trained with mini-batch descent gradient.
We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks.
arXiv Detail & Related papers (2022-07-19T05:47:30Z) - UR2KiD: Unifying Retrieval, Keypoint Detection, and Keypoint Description
without Local Correspondence Supervision [16.68130648568593]
Three related tasks, namely keypoint detection, description, and image retrieval can be jointly tackled using a single unified framework.
By leveraging diverse information from sequential layers of a standard ResNet-based architecture, we are able to extract keypoints and descriptors that encode local information.
Global information for image retrieval is encoded in an end-to-end pipeline, based on pooling of the aforementioned local responses.
arXiv Detail & Related papers (2020-01-20T21:01:38Z)
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.