BEST 2025 Abstracts

Area 1 - Blockchain and Enhanced Security for a New Era of Trust

Full Papers

Paper Nr:	5
Title:	A Comparative Analysis of Cryptographic Techniques for Privacy Preservation in Blockchain-Based Dispute Resolution
Authors:	Yassine Obeid, Christiana Zaraket and Layth Sliman
Abstract:	Digital innovations have profoundly altered the landscape of remote collaboration, yet these innovations frequently lead to conflicts that necessitate robust and fair decentralized decision-making systems.These conflicts occur either because they are not yet regulated by law, or because they arise from disputes associated with onchain applications, or simply because they are easier to resolve online. Public blockchains, characterized by their transparency and immutability, present viable solutions for these systems. However, the very transparency that makes blockchains appealing also introduces significant privacy concerns, as the traceability of transactions can jeopardize the anonymity of participants. Although users can maintain a degree of pseudoanonymity through cryptographic addresses, their activities remain publicly accessible, which poses considerable risks if their identities are uncovered. To mitigate these challenges, various cryptographic methods, including stealth addresses and zero-knowledge proofs (zk-SNARKs), have been developed to bolster transaction privacy. This paper distinguishes itself by offering an in-depth examination of these privacy-enhancing techniques, emphasizing their integration within blockchain environments, as well as their scalability and programmability. Additionally, we address key limitations, such as the balance between privacy and computational complexity, along with the interoperability issues that arise among privacy-centric protocols. By providing a comparative analysis and investigating future research avenues, this paper contributes valuable perspectives on reconciling privacy and transparency in decentralized collaboration frameworks.
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Paper Nr:	6
Title:	Trust-Centric Blockchain Framework: A Three-Layered Architecture for Securing Open and Private Systems
Authors:	Jihen Bennaceur, Wissem Zouaghi, Kawouther Thabet, Aziza Bennour, Roudha Ben Jemaa and Ali Mabrouk
Abstract:	Cybersecurity threats are becoming increasingly sophisticated, posing significant risks to both open and private architectures. Open architectures, such as open-source ecosystems, collaborative platforms, and information-sharing frameworks, thrive on transparency and accessibility but face challenges in maintaining trust, data integrity, and security. Conversely, private architectures, while operating within controlled environments, are not immune to internal threats or compromised trust mechanisms. Blockchain technology has emerged as a transformative solution to address these challenges by leveraging decentralization, immutability, and transparency. This paper introduces a dual Blockchain-based trust framework tailored to the unique needs of both open and private architectures. For open systems, the framework enhances trust and security through transparent trust modeling, Blockchain-based validation, and tamper-proof accountability mechanisms. For private systems, it strengthens internal trust evaluation and mitigates risks by securing interactions in a closed, permissioned environment. The proposed solution integrates three layers—trust modeling, Blockchain-enabled validation, and adaptive penalty mechanisms—to ensure robust security and participant accountability. Extensive simulations and security validations demonstrate the effectiveness of this approach across diverse open and private architecture scenarios, providing a comprehensive strategy to mitigate threats and reinforce trust in these critical ecosystems.
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Paper Nr:	8
Title:	Decentralized Car Rental System: Ensuring Security with Blockchain Smart Contracts
Authors:	Chiraz El Hog, Raoudha Ben Djemaa, Rania Aldaas, Buthainah Alnaeem, Rand Alageeli, Atheer Almutiri and Hadeel Alharbi
Abstract:	In today’s fast-paced digital economy, efficiency and trust are crucial, particularly in financial transactions and resource-sharing services. The growing interest in car-sharing platforms and blockchain technology has paved the way for decentralized solutions that enhance security, transparency, and automation. This paper presents a decentralized car rental solution built on blockchain technology, using smart contracts to ensure secure, tamper-proof, and trustful transactions. The proposed system allows users to browse and rent available vehicles, select suitable options based on their needs, and set rental terms seamlessly. Furthermore, car owners and businesses can rent out their unused vehicles for rent while specifying features, images, and pricing. The rental process is governed by blockchain-based identity management and smart contract enforcement, eliminating intermediaries and reducing operational risks. A key focus of this work is privacy and data protection. The system incorporates cryptographic techniques, access control mechanisms, and smart contract security best practices to mitigate risks such as fraud, unauthorized access, and data manipulation. Using blockchain immutability, decentralized identity verification, and secure payment channels, the platform improves user trust and prevents common vulnerabilities seen in traditional rental systems. Through this blockchain-based approach, we provide a secure, efficient, and transparent alternative to conventional car rental services, fostering greater trust among users while minimizing costs and enhancing operational efficiency.
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Short Papers

Paper Nr:	7
Title:	A Blockchain-Based Fraud Detection and Vehicle Damage Assessment System Using Machine Learning and Computer Vision
Authors:	Wafa Ben Slama Souei, N’Gouari Gana Abdou Bachir and Raoudha Ben Djemaa
Abstract:	Car insurance is a cornerstone of modern society, offering crucial financial protection in the event of accidents and vehicle-related damage. However, this intricate system is now grappling with a significant challenge: fraud manifesting in various forms, including staged accidents, fraudulent claims, and collusion between indi-viduals, and it poses a serious threat to the integrity and long-term viability of car insurance. In this paper, we will propose an innovative approach to fraud detection in car insurance and reimbursement estimation. The proposed approach makes significant contributions to the field by introducing a new dataset of 5,483 images with corresponding labels. Fraud detection is performed using the XGBoost Classifier, which is known for its robustness in handling complex classification tasks. Damage detection is carried out using the Mask R-CNN model, enabling precise identification and segmentation of vehicle damages. The system integrates structured data fraud detection with image-based damage assessment, where Mask R-CNN results serve as an additional validation factor. This end-to-end approach enhances fraud detection accuracy by combining data-driven insights with visual evidence for more reliable claim verification. These advancements contribute to improving the accuracy and efficiency of automated fraud detection and reimbursement estimation systems.
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