Open Access

ARTICLE

ORTHRUS: A Model for a Decentralized and Fair Data Marketplace Supporting Two Types of Output

Su Jin Shin¹, Sang Uk Shin^2,*

1 Department of Information Security, Graduate School, Pukyong National University, Busan, 48513, Republic of Korea
2 Division of Computer Engineering and Artificial Intelligence, Pukyong National University, Busan, 48513, Republic of Korea

* Corresponding Author: Sang Uk Shin. Email:

(This article belongs to the Special Issue: Cutting-Edge Security and Privacy Solutions for Next-Generation Intelligent Mobile Internet Technologies and Applications)

Computer Modeling in Engineering & Sciences 2025, 145(2), 2787-2819. https://doi.org/10.32604/cmes.2025.072602

Received 30 August 2025; Accepted 30 October 2025; Issue published 26 November 2025

Abstract

To reconstruct vehicle accidents, data from the time of the incident—such as pre-collision speed and collision point—is essential. This data is collected and generated through various sensors installed in the vehicle. However, it may contain sensitive information about the vehicle owner. Consequently, vehicle owners tend to be reluctant to provide their vehicle data due to concerns about personal information exposure. Therefore, extensive research has been conducted on secure vehicle data trading models. Existing models primarily utilize centralized approaches, leading to issues such as single points of failure, data leakage, and manipulation. To address these problems, this paper proposes ORTHRUS, a blockchain-based vehicle data trading marketplace that ensures transparency, traceability, and decentralization. The proposed model accommodates two categories of output data: the original data and the computed result from the function. Additionally, in the proposed model, data owners retain control over their data, enabling them to directly choose which types of data to provide. By employing Multi-party computation (MPC) technique, MOZAIK architecture, and the random leader selection technique, the proposed scheme, ORTHRUS, guarantees the input privacy and resistance to pre-collusion attacks. Furthermore, the proposed model promotes fairness by identifying dishonest behavior among participants by enforcing penalties and rewards through the implementation of smart contracts.

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Keywords

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