Research Topic

Security and Privacy on Blockchain-based AI

About this Research Topic

Blockchain and Artificial Intelligence are two technologies which, when merged, can help each other unleash their true potential. Blockchains can secure the availability and seamless access to integrity-protected large data sets, from various domains, thus facilitating the effective and more profound learning of AI algorithms. Similarly, AI can be utilized to introduce new consensus mechanisms, and therefore, new ways of interacting with Blockchains. However, when it comes to processing sensitive data, like corporate, healthcare and finance data, several security and privacy concerns arise that should be carefully considered. The interaction with Blockchains is subject to data credibility checks, data leakages related to transaction linkability, compliance with data protection regulations, on-chain data privacy, and malicious smart contracts. To address these challenges, new security and privacy-preserving solutions that are either based on AI or utilized for protecting AI-based blockchain data processing are emerging to facilitate the integration of these two cutting-edge technologies.

The aim of the current Research Topic is to cover promising, recent, and novel research trends in the fields of Blockchains and AI with an emphasis on security and privacy. More precisely, the goal is to attract new approaches, innovative machine learning and, generally, intelligent techniques that interact with blockchain technology to create advanced solutions that provide a secure environment for data processing and protect the privacy of individuals. Furthermore, the Research Topic is not limited only on these topics, but also covers issues related with secure decentralized and distributed machine learning, security- and privacy-by-design AI/Blockchain, privacy-preserving blockchain-based solutions, GDPR-compliant AI/Blockchain, and evaluation and optimization of AI/Blockchain solutions.

Areas to be covered in this Research Topic may include, but are not limited to:

● Privacy-preserving AI/Blockchain solutions
● Blockchain-based big data sharing for AI
● Privacy-preserving federated learning
● Blockchain-based AI
● Secure decentralized machine learning
● Secure distributed machine learning
● On-chain privacy
● Crypto-privacy AI techniques
● Smart contract privacy protection using AI
● Evaluating user privacy in Blockchains
● Cryptographic techniques for AI/Blockchain applications
● Differential privacy in AI/Blockchain
● GDPR strategy, compliance and optimization using AI/Blockchain
● Security- and privacy-by-design on AI/Blockchain


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Blockchain and Artificial Intelligence are two technologies which, when merged, can help each other unleash their true potential. Blockchains can secure the availability and seamless access to integrity-protected large data sets, from various domains, thus facilitating the effective and more profound learning of AI algorithms. Similarly, AI can be utilized to introduce new consensus mechanisms, and therefore, new ways of interacting with Blockchains. However, when it comes to processing sensitive data, like corporate, healthcare and finance data, several security and privacy concerns arise that should be carefully considered. The interaction with Blockchains is subject to data credibility checks, data leakages related to transaction linkability, compliance with data protection regulations, on-chain data privacy, and malicious smart contracts. To address these challenges, new security and privacy-preserving solutions that are either based on AI or utilized for protecting AI-based blockchain data processing are emerging to facilitate the integration of these two cutting-edge technologies.

The aim of the current Research Topic is to cover promising, recent, and novel research trends in the fields of Blockchains and AI with an emphasis on security and privacy. More precisely, the goal is to attract new approaches, innovative machine learning and, generally, intelligent techniques that interact with blockchain technology to create advanced solutions that provide a secure environment for data processing and protect the privacy of individuals. Furthermore, the Research Topic is not limited only on these topics, but also covers issues related with secure decentralized and distributed machine learning, security- and privacy-by-design AI/Blockchain, privacy-preserving blockchain-based solutions, GDPR-compliant AI/Blockchain, and evaluation and optimization of AI/Blockchain solutions.

Areas to be covered in this Research Topic may include, but are not limited to:

● Privacy-preserving AI/Blockchain solutions
● Blockchain-based big data sharing for AI
● Privacy-preserving federated learning
● Blockchain-based AI
● Secure decentralized machine learning
● Secure distributed machine learning
● On-chain privacy
● Crypto-privacy AI techniques
● Smart contract privacy protection using AI
● Evaluating user privacy in Blockchains
● Cryptographic techniques for AI/Blockchain applications
● Differential privacy in AI/Blockchain
● GDPR strategy, compliance and optimization using AI/Blockchain
● Security- and privacy-by-design on AI/Blockchain


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

19 July 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

19 July 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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