journal article·2023Open access

Enhancement of UAV Data Security and Privacy Via Ethereum Blockchain Technology

Sur Singh Rawat, Youseef Alotaibi, Nitima Malsa, Vimal Gupta

Computers, Materials & Continua· Volume 76· Issue 2

Abstract

Unmanned aerial vehicles (UAVs), or drones, have revolutionized a wide range of industries, including monitoring, agriculture, surveillance, and supply chain. However, their widespread use also poses significant challenges, such as public safety, privacy, and cybersecurity. Cyberattacks, targeting UAVs have become more frequent, which highlights the need for robust security solutions. Blockchain technology, the foundation of cryptocurrencies has the potential to address these challenges. This study suggests a platform that utilizes blockchain technology to manage drone operations securely and confidentially. By incorporating blockchain technology, the proposed method aims to increase the security and privacy of drone data. The suggested platform stores information on a public blockchain located on Ethereum and leverages the Ganache platform to ensure secure and private blockchain transactions. The MetaMask wallet for Ethbalance is necessary for BCT transactions. The present research finding shows that the proposed approach’s efficiency and security features are superior to existing methods. This study contributes to the development of a secure and efficient system for managing drone operations that could have significant applications in various industries. The proposed platform’s security measures could mitigate privacy concerns, minimize cyber security risk, and enhance public safety, ultimately promoting the widespread adoption of UAVs. The results of the study demonstrate that the blockchain can ensure the fulfillment of core security needs such as authentication, privacy preservation, confidentiality, integrity, and access control.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Blockchain Technology Applications and Security · UAV Applications and Optimization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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