{ "labelLang" : "hun", "responseDate" : "2024-03-28 22:03", "content" : { "otype" : "JournalArticle", "mtid" : 32588616, "status" : "VALIDATED", "published" : true, "comment" : "Department of Software Engineering, University of Szeged, Szeged, 6720, Hungary \n Department of Computational Optimization, University of Szeged, Szeged, 6720, Hungary \n Export Date: 12 January 2022 \n CODEN: FGCSE \n Correspondence Address: Baniata, H.; Department of Software Engineering, Hungary; email: baniatah@inf.u-szeged.hu \n Funding details: 5544 \n Funding details: Horizon 2020 Framework Programme, H2020, 957228 \n Funding details: Hungarian Scientific Research Fund, OTKA, FK 131793, OTKA FK 131793 \n Funding details: Horizon 2020 \n Funding details: European Regional Development Fund, ERDF \n Funding details: Magyarország Kormánya \n Funding details: National Research, Development and Innovation Office \n Funding text 1: This research work was supported by the Hungarian Scientific Research Fund , under the grant number OTKA FK 131793 , and by the TruBlo project of the European Union’s Horizon 2020 research and innovation program under grant agreement No. 957228 , and by the National Research, Development and Innovation Office within the framework of the Artificial Intelligence National Laboratory Programme, and by the University of Szeged Open Access Fund under the grant number 5544 . \n Funding text 2: This research work was supported by the Hungarian Scientific Research Fund under the grant number OTKA FK 131793 , and by the TruBlo project of the European Union’s Horizon 2020 research and innovation program under grant agreement No. 957228 , and by the National Research, Development and Innovation Office within the framework of the Artificial Intelligence National Laboratory Programme, and by the University of Szeged Open Access Fund under the grant number 5544 . \n Funding text 3: Attila Kertesz is currently with the University of Szeged, Szeged, Hungary. He is an associate professor at the Department of Software Engineering, leading the IoT-Cloud research group. He graduated as a program-designer mathematician in 2005, received his Ph.D. degree at the SZTE Doctoral School of Computer Science in 2011, and habilitated at the University of Szeged in 2017. His research interests include the federative management of Blockchain, IoT, Fog and Cloud systems, and interoperability issues of distributed systems in general. He is the leader of the FogBlock4Trust sub-grant project of the TruBlo EU H2020 project, and the OTKA FK 131793 national project financed by the Hungarian Scientific Research Fund, and a work package leader in the GINOPIoLT project, financed by the Hungarian Government and the European Regional Development Fund. He is also a Management Committee member of the CERCIRAS and INDAIRPOLLNET Cost Actions. 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The majority of network nodes need to be enforced to use one or both of the following approaches to remain consistent: (i) to wait for certain delays (i.e. by requesting a hard puzzle solution as in PoW and PoUW, or to wait for random delays as in PoET, etc.) (ii) to propagate shared data through shortest possible paths within the network. The first approach may cause higher energy consumption and/or lower throughput rates if not optimized, and in many cases these features are conventionally fixed. Therefore, it is preferred to enhance the second approach with some optimization. Previous works for this approach have the following drawbacks: they may violate the identity privacy of miners, only locally optimize the Neighbor Selection method (NS), do not consider the dynamicity of the network, or require the nodes to know the precise size of the network at all times. In this paper, we address these issues by proposing a Dynamic and Optimized NS protocol called DONS, using a novel privacy-aware leader election within the public BC called AnoLE, where the leader anonymously solves the The Minimum Spanning Tree problem (MST) of the network in polynomial time. Consequently, miners are informed about the optimum NS according to the current state of network topology. We analytically evaluate the complexity, the security and the privacy of the proposed protocols against state-of-the-art MST solutions for DLs and well known attacks. Additionally, we experimentally show that the proposed protocols outperform state-of-the-art NS solutions for public BCs. 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