Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2209.14200

Building an Ethereum-Based Decentralized Vehicle Rental System

N García-Moreno·P Caballero-Gil·C Caballero-Gil·J Molina-Gil
View original source ↗Read source document ↗Export metadata ↓

Abstract

Blockchain technology, beyond cryptocurrencies, is called to be the new information exchange ecosystem due to its unique properties, such as immutability and transparency. The main objective of this work is to introduce the design of a decentralized rental system, which leverages smart contracts and the Ethereum public blockchain. The work started from an exhaustive investigation on the Ethereum platform, emphasizing the aspect of cryptography and all the technology behind this platform. In order to test the proposed scheme in a realistic use, the implementation of a web application for the rental of vehicles has been carried out. The application covers the entire vehicle rental process offered in traditional web applications, adding more autonomy and ease of use to users. Following Ethereum application development guidelines, all business logic is located in the smart contracts implemented in the Ethereum network, where these contracts control the entire vehicle rental system of customers. While this is a work in progress, the results obtained in the first proof of concept have been very promising.

Explore related subjects

cs.CR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

N García-Moreno, P Caballero-Gil, C Caballero-Gil, J Molina-Gil. 2022-09-28. Building an Ethereum-Based Decentralized Vehicle Rental System. https://arxiv.org/abs/2209.14200

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

A Survey of Threats Against Voice Authentication and Anti-Spoofing Systems

Voice authentication has undergone significant changes from traditional systems that relied on handcrafted acoustic features to deep learning models that can extract robust speaker embeddings. This advancement has expanded its applications across finance, smart devices, law enforcement, and beyond. However, as adoption has grown, so have the threats. This survey presents a comprehensive review of the modern threat landscape targeting Voice Authentication Systems (VAS) and Anti-Spoofing Countermeasures (CMs), including data poisoning, adversarial, deepfake, and adversarial spoofing attacks. We chronologically trace the development of voice authentication and examine how vulnerabilities have evolved in tandem with technological advancements. For each category of attack, we summarize methodologies, highlight commonly used datasets, compare performance and limitations, and organize existing literature using widely accepted taxonomies. By highlighting emerging risks and open challenges, this survey aims to support the development of more secure and resilient voice authentication systems.

cs.CR↗
arXiv2026

SkillScope: Toward Fine-Grained Least-Privilege Enforcement for Agent Skills

Agent Skills have become a practical way to extend LLM agents by packaging metadata, natural-language instructions, and executable resources into reusable capability bundles. However, this growing Skill ecosystem introduces a new compliance risk: a Skill may perform high-impact actions that fall outside the scope permitted by the user's current request, thereby violating least privilege. Existing skill detection approaches are insufficient for this problem because it is inherently task-conditioned: the same action may be legitimate under one user prompt but over-privileged under another. In this paper, we present SkillScope, a framework for fine-grained least-privilege enforcement in Agent Skills. SkillScope adopts a graph-based analysis approach that models instruction-level procedures and code-level operations as fine-grained action nodes. It extracts potential over-privilege candidates, validates them under graph-instantiated user tasks through runtime analysis, and constrains validated over-privileged actions via control-flow privilege constraining. We evaluate SkillScope through effectiveness experiments and large-scale real-world measurement. SkillScope achieves a 94.53% skill-level F1 score for over-privilege detection. In the wild, SkillScope validates 6,590 of 68,312 valid real-world Skills as exhibiting over-privileged behaviors, showing that least-privilege violations are prevalent in current Skill ecosystems. In the privilege-constraining evaluation, SkillScope reduces triggered over-privileged action-in-task instances by 88.56% while preserving legitimate task completion.

cs.CR↗
arXiv2026

Privacy Auditing with Zero (0) Training Run

Privacy auditing provides empirical lower bounds on the differential privacy parameters of learning algorithms. Existing methods, however, require interventional access to the training pipeline, either to retrain models multiple times or to randomize data inclusion. This is often infeasible for large and opaque deployed systems such as foundation or language models. We introduce Zero-Run privacy auditing, a framework for auditing models post-hoc using two fixed datasets: examples known to be training-set members and examples known to be non-members. In this observational regime, membership is no longer randomized; instead, member and non-member data often differ in distribution, so membership inference scores may reflect a distribution shift rather than algorithmic leakage. Drawing on ideas from causal inference, we formalize this confounding effect and propose two complementary corrections that yield valid privacy audits. Our first approach models the combined effect of distribution shift and algorithmic leakage as an adaptive composition, producing conservative global corrections. Our second approach conditions on observed data and adjusts pointwise membership guesses, yielding sharper instance-dependent bounds. Experiments on synthetic data and large-scale image and text models show that Zero-Run auditing enables practical privacy evaluation when retraining or controlled data insertion is infeasible.

cs.CR↗