Towards a Trusted and Unified Consortium-Blockchain-Based Data Sharing Infrastructure for Open Learning—TolFob Architecture and Implementation

Open learning is now facing a complex higher education ecosystem that involves a variety of heterogeneous information systems and comprises decentralized stakeholders, such as universities, professors, students, and software vendors. Authentic, non-repudiable, and fast available data sharing among open learning information systems and stakeholders is a key issue that remains unresolved. To solve this problem, this paper proposes a consortium blockchain extended architecture featuring integration and cross-chain functions to provide a unified and trusted data-sharing infrastructure for open learning. The overall architecture consists of three elements: a blockchain-integrated open learning scenario schema; a blockchain-integrated open learning application model; and a pragmatic blockchain integration framework. The proposed blockchain integration framework is implemented based on Hyperledger Fabric 1.4. A trusted open-learning behavior and achievement management application is developed as a proof-of-concept which integrates two educational institutions’ four productional learning systems into a blockchain network and has stably run over six months. A suite of experiments is designed and executed to verify our blockchain system’s viability and scalability. The test result shows the implementation of the blockchain system is competent for the production environment and outperforms related works investigated. However, it does have limitations and optimization potential, which will be studied in the future.

A Decentralized Identity-Based Blockchain Solution for Privacy-Preserving Licensing of Individual-Controlled Data to Prevent Unauthorized Secondary Data Usage

This paper presents a design for a blockchain solution aimed at the prevention of unauthorized secondary use of data. This solution brings together advances from the fields of identity management, confidential computing, and advanced data usage control. In the area of identity management, the solution is aligned with emerging decentralized identity standards: decentralized identifiers (DIDs), DID communication and verifiable credentials (VCs). In respect to confidential computing, the Cheon-Kim-Kim-Song (CKKS) fully homomorphic encryption (FHE) scheme is incorporated with the system to protect the privacy of the individual’s data and prevent unauthorized secondary use when being shared with potential users. In the area of advanced data usage control, the solution leverages the PRIV-DRM solution architecture to derive a novel approach to licensing of data usage to prevent unauthorized secondary usage of data held by individuals. Specifically, our design covers necessary roles in the data-sharing ecosystem: the issuer of personal data, the individual holder of the personal data (i.e., the data subject), a trusted data storage manager, a trusted license distributor, and the data consumer. The proof-of-concept implementation utilizes the decentralized identity framework being developed by the Hyperledger Indy/Aries project. A genomic data licensing use case is evaluated, which shows the feasibility and scalability of the solution.

Integrity protection method for trusted data of IOT nodes based on transfer learning

In order to overcome the problems of high data storage occupancy and long encryption time in traditional integrity protection methods for trusted data of IOT node, this paper proposes an integrity protection method for trusted data of IOT node based on transfer learning. Through the transfer learning algorithm, the data characteristics of the IOT node is obtained, the feature mapping function in the common characteristics of the node data is set to complete the classification of the complete data and incomplete data in the IOT nodes. The data of the IOT nodes is input into the data processing database to verify its security, eliminate the node data with low security, and integrate the security data and the complete data. On this basis, homomorphic encryption algorithm is used to encrypt the trusted data of IOT nodes, and embedded processor is added to the IOT to realize data integrity protection. The experimental results show that: after using the proposed method to protect the integrity of trusted data of IOT nodes, the data storage occupancy rate is only about 3.5%, the shortest time-consuming of trusted data encryption of IOT nodes is about 3 s, and the work efficiency is high.

Blockchain for Electric Vehicles Energy Trading: Requirements, Opportunities, and Challenges

Today's technologies, techniques, and systems leveraged for managing energy trading operations in electric vehicles fall short in providing operational transparency, immutability, fault tolerance, traceability, and trusted data provenance features. They are centralized and vulnerable to the single point of failure problem, and less trustworthy as they are prone to the data modifications and deletion by adversaries. In this paper, we present the potential advantages of blockchain technology to manage energy trading operations between electric vehicles as it can offer data traceability, immutability, transparency, audit, security, and confidentiality in a fully decentralized manner. We identify and discuss the essential requirements for the successful implementation of blockchain technology to secure energy trading operations among electric vehicles. We present a detailed discussion on the potential opportunities offered by blockchain technology to secure the energy trading operations of electric vehicles. We discuss several blockchain-based research projects and case studies to highlight the practicability of blockchain technology in electric vehicles energy trading. Finally, we identify and discuss open challenges in fulfilling the requirements of electric vehicles energy trading applications.

Blockchain for Deep Learning: Review and Open Challenges

Deep learning has gained huge traction in recent years because of its potential to make informed decisions. A large portion of today's deep learning systems are based on centralized servers and fall short in providing operational transparency, traceability, reliability, security, and trusted data provenance features. Also, training deep learning models by utilizing centralized data is vulnerable to the single point of failure problem. In this paper, we explore the importance of integrating blockchain technology with deep learning. We review the existing literature focused on the integration of blockchain with deep learning. We classify and categorize the literature by devising a thematic taxonomy based on seven parameters; namely, blockchain type, deep learning models, deep learning specific consensus protocols, application area, services, data types, and deployment goals. We provide insightful discussions on the state-of-the-art blockchain-based deep learning frameworks by highlighting their strengths and weaknesses. Furthermore, we compare the existing blockchain-based deep learning frameworks based on four parameters such as blockchain type, consensus protocol, deep learning method, and dataset. Finally, we present important research challenges which need to be addressed to develop highly efficient, robust, and secure deep learning frameworks.

Blockchain for Deep Learning: Review and Open Challenges

Deep learning has gained huge traction in recent years because of its potential to make informed decisions. A large portion of today's deep learning systems are based on centralized servers and fall short in providing operational transparency, traceability, reliability, security, and trusted data provenance features. Also, training deep learning models by utilizing centralized data is vulnerable to the single point of failure problem. In this paper, we explore the importance of integrating blockchain technology with deep learning. We review the existing literature focused on the integration of blockchain with deep learning. We classify and categorize the literature by devising a thematic taxonomy based on seven parameters; namely, blockchain type, deep learning models, deep learning specific consensus protocols, application area, services, data types, and deployment goals. We provide insightful discussions on the state-of-the-art blockchain-based deep learning frameworks by highlighting their strengths and weaknesses. Furthermore, we compare the existing blockchain-based deep learning frameworks based on four parameters such as blockchain type, consensus protocol, deep learning method, and dataset. Finally, we present important research challenges which need to be addressed to develop highly efficient, robust, and secure deep learning frameworks.

Should Gatekeepers Be Allowed to Combine Data? Ideas for Art. 5(a) of the Draft Digital Markets Act

Should digital gatekeepers be allowed to gather data from users and combine data from different sources? That is one of the key substantive questions of the Digital Markets Act (DMA). It is currently addressed in Art. 5(a) of the draft DMA. There are two problems with the current wording: first, it is not specific enough to work as a self-executable provision; secondly, it could happen that users are nudged into giving consent easily so that the gatekeepers can continue to expand their sets of personal data, without users having a ‘real’ say and with third parties losing out in competition. In this contribution, I analyse this problem. My suggestion is to resort to a ‘rating solution’: qualified entities, e.g. trusted data intermediaries, should rate, certify or label the data options offered by the gatekeepers and serve as ‘data guides’ for consumers. I also look at other policy options.