Special Issue "Advances in Blockchain Technology and Applications"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 March 2019

Special Issue Editors

Guest Editor
Dr. Roberto Tonelli

Department of Mathematics and Informatics, University of Cagliari, Italy
Website | E-Mail
Interests: complex software systems; software engineering; blockchain and smart contracts
Guest Editor
Prof. Michele Marchesi

Department of Mathematics and Informatics, University of Cagliari, Italy
Website | E-Mail
Interests: complex software systems; software engineering; blockchain and smart contracts; econophysics

Special Issue Information

Dear Colleagues,

The disrupting power of Blockchain technology is affecting and influencing our world, day by day. The number of researchers, software developers, and companies involved in this new technology is increasing exponentially, and this is also reflected by the number of projects that start every day, aimed at creating new blockchains, or new applications, built on existing blockchains. However, research papers devoted to the investigation of the various blockchains and the related programming languages used for coding in the Blockchain environment, though increasing, are still not enough. Therefore, we believe that initiatives aimed at inviting and putting together scientific works on this new technology should be encouraged. For this reason, we warmly welcome the proposal to edit a Special Issue of Applied Sciences on this subject.

The goal of the Special Issue is to gather together sound scientific papers on the theoretical and practical aspects related to blockchain technologies and smart contracts. The main focus is on the application to blockchain and smart contracts development of studies pertaining the relevant main areas of computer science. Topics may include, but are not limited to:

  • Blockchain and Dapp architectures.
  • Blockchain scaling and interplay.
  • Decentralized artificial intelligence.
  • Decentralized databases.
  • Static and dynamic blockchain analysis; blockchain forensics.
  • Big data and the blockchain.
  • Human-computer interaction for blockchain applications.
  • Blockchain and the Internet of things.
  • Languages for smart contract development.
  • Blockchain-oriented software engineering.
  • Blockchain security and testing.
  • Decentralized applications in finance, banking, insurance, supply chain management, e-government, notarization, industry, energy, logistic, commerce, gaming, education.

Dr. Roberto Tonelli
Prof. Michele Marchesi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Blockchain
  • Smart Contracts
  • Dapps
  • Distributed Ledger Technology
  • DLT
  • Decentralized applications

Published Papers (4 papers)

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Research

Open AccessArticle MedChain: Efficient Healthcare Data Sharing via Blockchain
Appl. Sci. 2019, 9(6), 1207; https://doi.org/10.3390/app9061207 (registering DOI)
Received: 27 November 2018 / Revised: 22 December 2018 / Accepted: 28 December 2018 / Published: 22 March 2019
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Abstract
Healthcare information exchange is an important research topic, which can benefit both healthcare providers and patients. In healthcare data sharing, many cloud-based solutions have been proposed, but the trustworthiness of a third-party cloud service is questionable. Recently, blockchain has been introduced in healthcare [...] Read more.
Healthcare information exchange is an important research topic, which can benefit both healthcare providers and patients. In healthcare data sharing, many cloud-based solutions have been proposed, but the trustworthiness of a third-party cloud service is questionable. Recently, blockchain has been introduced in healthcare record sharing, which does not rely on trusting a third party. However, existing approaches only focus on the records collected from medical examination. They are not efficient in sharing data streams continuously generated from sensors and other monitoring devices. Today, IoT devices have been widely deployed and sensors and mobile applications can monitor patients’ body conditions. The collected data are shared to laboratories and institutions for diagnosis and further study. Moreover, existing approaches are too rigid to efficiently support metadata change. In this paper, an efficient data-sharing scheme is proposed, called MedChain, which combines blockchain, digest chain, and structured P2P network techniques to overcome the above efficiency issues in the existing approaches for sharing both types of healthcare data. Based on MedChain, a session-based healthcare data-sharing scheme is devised, which brings flexibility in data sharing. The evaluation results show that MedChain can achieve higher efficiency and satisfy the security requirements in data sharing. Full article
(This article belongs to the Special Issue Advances in Blockchain Technology and Applications)
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Open AccessArticle Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study
Appl. Sci. 2018, 8(12), 2663; https://doi.org/10.3390/app8122663
Received: 4 November 2018 / Revised: 14 December 2018 / Accepted: 14 December 2018 / Published: 18 December 2018
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Abstract
The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts [...] Read more.
The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the local machine learning models with malicious training samples, thereby influencing the outcome of the federated learning and evading detection. We present a solution where contributing parties in federated learning can be held accountable and have their model updates audited. We describe a permissioned blockchain-based federated learning method where incremental updates to an anomaly detection machine learning model are chained together on the distributed ledger. By integrating federated learning with blockchain technology, our solution supports the auditing of machine learning models without the necessity to centralize the training data. Experiments with a realistic intrusion detection use case and an autoencoder for anomaly detection illustrate that the increased complexity caused by blockchain technology has a limited performance impact on the federated learning, varying between 5 and 15%, while providing full transparency over the distributed training process of the neural network. Furthermore, our blockchain-based federated learning solution can be generalized and applied to more sophisticated neural network architectures and other use cases. Full article
(This article belongs to the Special Issue Advances in Blockchain Technology and Applications)
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Open AccessArticle Blockchain: A Tale of Two Applications
Appl. Sci. 2018, 8(9), 1506; https://doi.org/10.3390/app8091506
Received: 31 July 2018 / Revised: 21 August 2018 / Accepted: 24 August 2018 / Published: 1 September 2018
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Abstract
Bitcoin continues to get more and more attention from the media, mainly because of the volatility of its value and insignificantly associated with the technological innovation. This cryptocurrency is supported by an immutable database and is distributed throughout a network of thousands of [...] Read more.
Bitcoin continues to get more and more attention from the media, mainly because of the volatility of its value and insignificantly associated with the technological innovation. This cryptocurrency is supported by an immutable database and is distributed throughout a network of thousands of nodes, known as Blockchain. One way to ensure that all the concepts behind the Blockchain technology and infrastructure are seized is to conduct the development of one of the most popular context applications for it: a wallet for well-known cryptocurrencies. Yet Another Bitcoin Wallet (YABW) is a hybrid application available for both Android and iOS, which was developed with the Ionic and Angular frameworks. This application communicates with Bitcoin Blockchain to send, receive and store bitcoins; provides a set of features focused on security and user experience, and is available on the Play Store and Apple Store. A rather relevant issue that is becoming a major subject of current research is the application of the Blockchain infrastructure to other contexts that are neither directly connected to cryptocurrencies, nor are finance related. The implementation of a proof-of-concept application proposes the use of a blockchain for a specific case study: the exchange of meal vouchers of an institution amongst students. This is achieved using the decentralized platform Ethereum, which allows us to create a Smart Contract using the Solidity programming language to create a token that follows the Ethereum Request for Comment (ERC), the ERC-20 standard and represents the meal vouchers. This second application uses the architecture defined for YABW, reusing major components and custom developing specific modules to enhance the required features. There is still a lot of research to be done on the non-financial applicability of the Blockchain infrastructure and technology, but for the moment, we have left further evidence that it is possible and is a relative straight-forward process to accomplish from the technological perspective. Full article
(This article belongs to the Special Issue Advances in Blockchain Technology and Applications)
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Open AccessArticle BAQALC: Blockchain Applied Lossless Efficient Transmission of DNA Sequencing Data for Next Generation Medical Informatics
Appl. Sci. 2018, 8(9), 1471; https://doi.org/10.3390/app8091471
Received: 22 July 2018 / Revised: 15 August 2018 / Accepted: 23 August 2018 / Published: 27 August 2018
PDF Full-text (2355 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Due to the development of high-throughput DNA sequencing technology, genome-sequencing costs have been significantly reduced, which has led to a number of revolutionary advances in the genetics industry. However, the problem is that compared to the decrease in time and cost needed for [...] Read more.
Due to the development of high-throughput DNA sequencing technology, genome-sequencing costs have been significantly reduced, which has led to a number of revolutionary advances in the genetics industry. However, the problem is that compared to the decrease in time and cost needed for DNA sequencing, the management of such large volumes of data is still an issue. Therefore, this research proposes Blockchain Applied FASTQ and FASTA Lossless Compression (BAQALC), a lossless compression algorithm that allows for the efficient transmission and storage of the immense amounts of DNA sequence data that are being generated by Next Generation Sequencing (NGS). Also, security and reliability issues exist in public sequence databases. For methods, compression ratio comparisons were determined for genetic biomarkers corresponding to the five diseases with the highest mortality rates according to the World Health Organization. The results showed an average compression ratio of approximately 12 for all the genetic datasets used. BAQALC performed especially well for lung cancer genetic markers, with a compression ratio of 17.02. BAQALC performed not only comparatively higher than widely used compression algorithms, but also higher than algorithms described in previously published research. The proposed solution is envisioned to contribute to providing an efficient and secure transmission and storage platform for next-generation medical informatics based on smart devices for both researchers and healthcare users. Full article
(This article belongs to the Special Issue Advances in Blockchain Technology and Applications)
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