Secure Applications with Blockchain and Artificial Intelligence

Topic Information

Dear Colleagues,

Blockchain technology, which allows untrusted individuals to connect with others in a verifiable manner, has attracted much attention from both academia and industry. It is now used commercially for various applications such as tracking ownership, digital assets and voting rights. Blockchain is transforming our daily lives in every aspect, including banks and other financial institutions, hospitals, companies, and governments, among others. To make a blockchain application smart, Artificial Intelligence (AI) is being widely studied, which can build smart machines capable of performing tasks that typically require human intelligence, such as the ability to reason or to learn from past experience. For instance, AI algorithms can help blockchain applications handle data more efficiently without human intervention. The intelligence provided by AI can also benefit the design of smart contract in blockchains. Now it is possible to create deep learning neural networks, which can perform quickly and accurately for real-world blockchain applications.

The use of blockchain can provide various merits, such as transparency, data integrity, and enhanced security. By combining AI with blockchain, there is potential to make a real-world system more secure, intelligent, and efficient. For instance, blockchain can be used to ensure the integrity of shared data or models that would be used by AI, including deep learning and many machine learning techniques. The blockchain-empowered AI system is believed to be more robust against adversarial attacks. However, such a combination is not mature at the current stage, and many challenges remain unsolved, which are highlighted as follows: (1) Blockchain suffers many performance limitations, i.e., when the nodes or transactions become large, its efficiency is significantly degraded. (2) Both blockchain- and AI-based systems may leak private information, especially when the system aggregates data from various nodes. (3) Blockchain and AI systems are the main targets for cyber attackers, and various attacks have been posed on these systems, such as the 51% attack, double spending attacks, etc. Hence, how to combine blockchain with AI in a secure and intelligent way needs more research efforts.

This Topic focuses on using blockchain and AI to secure applications, software, data and systems. We invite original research, practice and surveys that investigate securing applications with blockchain and artificial intelligence. The topics of interest include, but are not limited to:

• Machine learning for privacy preservation and system security
• Data security and privacy on blockchain
• Artificial intelligence models for blockchain systems
• AI-empowered secure blockchain applications
• AI-empowered blockchain in forensics
• Blockchain and AI for intrusion detection
• Digital preservation with AI and blockchain
• AI-driven smart contracts
• Secure computing on AI-empowered blockchain
• AI-driven secure applications, data, software and systems
• Availability, recovery and auditing with blockchain and AI
• Trust management with blockchain and AI
• Privacy protection with blockchain and AI
• Blockchain and AI-secured systems
• Secure applications supported by information theory or entropy
• Blockchain or AI systems by applying information theory or entropy

Prof. Dr. Zheng Yan
Dr. Xiaokang Zhou
Dr. Weizhi Meng
Topic Editors

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Entropy entropy	2.7	4.7	1999	20.8 Days	CHF 2600
Future Internet futureinternet	3.4	6.7	2009	11.8 Days	CHF 1600
Algorithms algorithms	2.3	3.7	2008	15 Days	CHF 1600
Information information	3.1	5.8	2010	18 Days	CHF 1600
Machine Learning and Knowledge Extraction make	3.9	8.5	2019	19.9 Days	CHF 1800

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Published Papers (4 papers)

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All Entropy Future Internet Algorithms Information MAKE

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13 pages, 1314 KiB  

Open AccessReview

Artificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate

by Mohammad Mohammad Amini, Marcia Jesus, Davood Fanaei Sheikholeslami, Paulo Alves, Aliakbar Hassanzadeh Benam and Fatemeh Hariri

Mach. Learn. Knowl. Extr. 2023, 5(3), 1023-1035; https://doi.org/10.3390/make5030053 - 07 Aug 2023

Cited by 8 | Viewed by 5565

Abstract

This study examines the ethical issues surrounding the use of Artificial Intelligence (AI) in healthcare, specifically nursing, under the European General Data Protection Regulation (GDPR). The analysis delves into how GDPR applies to healthcare AI projects, encompassing data collection and decision-making stages, to [...] Read more.

This study examines the ethical issues surrounding the use of Artificial Intelligence (AI) in healthcare, specifically nursing, under the European General Data Protection Regulation (GDPR). The analysis delves into how GDPR applies to healthcare AI projects, encompassing data collection and decision-making stages, to reveal the ethical implications at each step. A comprehensive review of the literature categorizes research investigations into three main categories: Ethical Considerations in AI; Practical Challenges and Solutions in AI Integration; and Legal and Policy Implications in AI. The analysis uncovers a significant research deficit in this field, with a particular focus on data owner rights and AI ethics within GDPR compliance. To address this gap, the study proposes new case studies that emphasize the importance of comprehending data owner rights and establishing ethical norms for AI use in medical applications, especially in nursing. This review makes a valuable contribution to the AI ethics debate and assists nursing and healthcare professionals in developing ethical AI practices. The insights provided help stakeholders navigate the intricate terrain of data protection, ethical considerations, and regulatory compliance in AI-driven healthcare. Lastly, the study introduces a case study of a real AI health-tech project named SENSOMATT, spotlighting GDPR and privacy issues. Full article

(This article belongs to the Topic Secure Applications with Blockchain and Artificial Intelligence)

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25 pages, 4135 KiB  

Open AccessArticle

A Secure Scheme Based on a Hybrid of Classical-Quantum Communications Protocols for Managing Classical Blockchains

by Ang Liu, Xiu-Bo Chen, Shengwei Xu, Zhuo Wang, Zhengyang Li, Liwei Xu, Yanshuo Zhang and Ying Chen

Entropy 2023, 25(5), 811; https://doi.org/10.3390/e25050811 - 17 May 2023

Viewed by 1296

Abstract

Blockchain technology affords data integrity protection and building trust mechanisms in transactions for distributed networks, and, therefore, is seen as a promising revolutionary information technology. At the same time, the ongoing breakthrough in quantum computation technology contributes toward large-scale quantum computers, which might [...] Read more.

Blockchain technology affords data integrity protection and building trust mechanisms in transactions for distributed networks, and, therefore, is seen as a promising revolutionary information technology. At the same time, the ongoing breakthrough in quantum computation technology contributes toward large-scale quantum computers, which might attack classic cryptography, seriously threatening the classic cryptography security currently employed in the blockchain. As a better alternative, a quantum blockchain has high expectations of being immune to quantum computing attacks perpetrated by quantum adversaries. Although several works have been presented, the problems of impracticality and inefficiency in quantum blockchain systems remain prominent and need to be addressed. First, this paper develops a quantum-secure blockchain (QSB) scheme by introducing a consensus mechanism—quantum proof of authority (QPoA) and an identity-based quantum signature (IQS)—wherein QPoA is used for new block generation and IQS is used for transaction signing and verification. Second, QPoA is developed by adopting a quantum voting protocol to achieve secure and efficient decentralization for the blockchain system, and a quantum random number generator (QRNG) is deployed for randomized leader node election to protect the blockchain system from centralized attacks like distributed denial of service (DDoS). Compared to previous work, our scheme is more practical and efficient without sacrificing security, greatly contributing to better addressing the challenges in the quantum era. Extensive security analysis demonstrates that our scheme provides better protection against quantum computing attacks than classic blockchains. Overall, our scheme presents a feasible solution for blockchain systems against quantum computing attacks through a quantum strategy, contributing toward quantum-secured blockchain in the quantum era. Full article

(This article belongs to the Topic Secure Applications with Blockchain and Artificial Intelligence)

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16 pages, 2996 KiB  

Open AccessArticle

Cross-Domain Identity Authentication Protocol of Consortium Blockchain Based on Face Recognition

by Xiang Chen, Shouzhi Xu, Kai Ma and Peng Chen

Information 2022, 13(11), 535; https://doi.org/10.3390/info13110535 - 10 Nov 2022

Viewed by 1829

Abstract

A consortium system can leverage information to improve workflows, accountability, and transparency through setting up a backbone for these cross-company and cross-discipline solutions, which make it become a hot spot of market application. Users of a consortium system may register and log in [...] Read more.

A consortium system can leverage information to improve workflows, accountability, and transparency through setting up a backbone for these cross-company and cross-discipline solutions, which make it become a hot spot of market application. Users of a consortium system may register and log in different target domains to get the access authentications, so how to access resources in different domains efficiently to avoid the trust-island problem is a big challenge. Cross-domain authentication is a kind of technology that breaks trust islands and enables users to access resources and services in different domains with the same credentials, which reduces service costs for all parties. Aiming at the problems of traditional cross-domain authentication, such as complex certificate management, low authentication efficiency, and being unable to prevent the attack users’ accounts, a cross-domain authentication protocol based on face recognition is proposed in this paper. The protocol makes use of the decentralized and distributed characteristics of the consortium chain to ensure the reliable transmission of data between participants without trust relationships, and achieves biometric authentication to further solve the problem of account attack by applying a deep-learning face-recognition model. An asymmetric encryption algorithm is used to encrypt and store the face feature codes on the chain to ensure the privacy of the user’s face features. Finally, through security analysis, it is proved that the proposed protocol can effectively prevent a man-in-the-middle attack, a replay attack, an account attack, an internal attack, and other attacks, and mutual security authentication between different domains can be realized with the protocol. Full article

(This article belongs to the Topic Secure Applications with Blockchain and Artificial Intelligence)

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15 pages, 317 KiB  

Open AccessArticle

Research on Factors Affecting SMEs’ Credit Risk Based on Blockchain-Driven Supply Chain Finance

by Ping Xiao, Mad Ithnin bin Salleh and Jieling Cheng

Information 2022, 13(10), 455; https://doi.org/10.3390/info13100455 - 27 Sep 2022

Cited by 8 | Viewed by 2615

Abstract

The development of blockchain-driven supply chain finance aimed to solve the financing problems of SMEs. However, credit risk is expanded, and even transmitted to the whole supply chain, due to their connection, so that it becomes more difficult to effectively identify the credit [...] Read more.

The development of blockchain-driven supply chain finance aimed to solve the financing problems of SMEs. However, credit risk is expanded, and even transmitted to the whole supply chain, due to their connection, so that it becomes more difficult to effectively identify the credit risk of SMEs. The purpose of this paper was to examine the factors affecting SMEs’ credit risk in the mode of block-chain-driven supply chain finance. This research proposed an entropy weight method to construct independent variables and used logistic regression to examine whether the financing enterprises, core enterprises, assets position under financing, blockchain platform, and supply chain operation have significant impacts on credit risk. The panel data, originating from CSMAR on fifty-six quoted SMEs, included eight core enterprises and twenty-six blockchain enterprises, between 2016 and 2020. The results showed that the financing enterprises, core enterprises, asset position under fi-nance, blockchain platform, and supply chain operation have significant impacts on SMEs’ credit risk when the confidence level is 90%. The financial status of financing enterprises can reflect the credit status of SMEs. Core enterprises give credit guarantees to SMEs, and the business transactions between SMEs and core enterprises affect the credit risk through the asset position under financing. Meanwhile, blockchain platforms can solve the problem of the information asymmetry of the par-ticipating enterprises in supply chain operations. At the same time, the supply chain operation is also an important factor affecting the credit risk. This conclusion provides a reference for the ap-plication of blockchains in supply chains, to reduce the credit risk. At the same time, the selected indicators were more comprehensive, which provided a strong basis for the subsequent construc-tion of a credit risk assessment model using key factors. Full article

(This article belongs to the Topic Secure Applications with Blockchain and Artificial Intelligence)

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