Delegation-Based Personal Data Processing Request Notarization Framework for GDPR Based on Private Blockchain

Jung, Sung-Soo; Lee, Sang-Joon; Euom, Ieck-Chae

doi:10.3390/app112210574

Cited by 7 publications

(7 citation statements)

References 34 publications

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“…The BC system conducts an autonomous verification process, commonly referred to as an endorsement, prior to granting approval for a transaction. This verification process is of utmost importance in maintaining the security of the system [10]. It also makes consortium-based transactions between organizations possible through smart contracts, which is a very important way to make communication between service providers easier.…”

Section: Decentralized Aggregatormentioning

confidence: 99%

An Optimized and Scalable Blockchain-Based Distributed Learning Platform for Consumer IoT

Wang,

Liu,

Shao

et al. 2023

Mathematics

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Consumer Internet of Things (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem, like a smart home. Due to security and privacy concerns, blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their machine learning (ML) models using end-user data. Federated learning (FL) uses privacy-preserving ML techniques to forecast customers’ needs and consumption habits, and blockchain replaces the centralized aggregator to safeguard the ecosystem. However, blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research introduces a novel architecture, emphasizing gateway peer (GWP) in the blockchain network to address scalability, ledger optimization, and secure model transmission issues. In the architecture, we replace the centralized aggregator with the blockchain network, while GWP limits the number of local transactions to execute in BCN. Considering the security and privacy of FL processes, we incorporated differential privacy and advanced normalization techniques into ML processes. These approaches enhance the cybersecurity of end-users and promote the adoption of technological innovation standards by service providers. The proposed approach has undergone extensive testing using the well-respected Stanford (CARS) dataset. We experimentally demonstrate that the proposed architecture enhances network scalability and significantly optimizes the ledger. In addition, the normalization technique outperforms batch normalization when features are under DP protection.

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Section: Decentralized Aggregatormentioning

confidence: 99%

An Optimized and Scalable Blockchain-Based Distributed Learning Platform for Consumer IoT

Wang,

Liu,

Shao

et al. 2023

Mathematics

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“…The General Data Protection Regulation (GDPR) is the most important regulation regarding data protection and privacy in the European Union. Ensuring the reliability and integrity of the personal data processing request records of a data subject to enable its utilization according to the GDPR requirements is the challenge investigated in [15]. In this paper, the authors propose a notarization framework using a private blockchain to allow the data subject to delegate requests to process personal data.…”

Section: Regulationmentioning

confidence: 99%

Advances in Information Security and Privacy

Lax

Russo

2022

Applied Sciences

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“…The risk of individual rights violation has increased with the sharing of unnecessary personal data [35,47].…”

Section: Restricted Records Data Retrievalmentioning

confidence: 99%

“…Sharing personal data between the participants as data controllers recorded at each transmission stage should be immutable and transparent on the history of transactions [20,43,47].…”

Section: Audit Logs Track Activitiesmentioning

confidence: 99%

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Formal models for consent management in healthcare software system development

Peyrone

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In the era of data-driven opportunities, many businesses are missing the data-privacy challenge, which leads to risks in safeguarding their customers' data. To empower individuals (data subjects) to control their data, the General Data Protection Regulation (GDPR) mandated businesses or organizations (data controllers) to protect individuals' data (personal data) within data protection law. Nevertheless, many businesses still struggle to enhance and develop their software systems to comply with the GDPR because it is difficult to interpret and apply to software development practices. Besides, the processing of personal data begins when the data subject provides explicit consent to the data controller, which makes consent management (CM) essential for conducting the personal data lifecycle. This thesis aims to fill this gap by proposing formal models and translating them into class diagrams for consent management in centralized systems and data sharing in distributed systems as guidelines for software engineers. Moreover, the proposed models have been verified and described behavior using the Event-B method.

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Delegation-Based Personal Data Processing Request Notarization Framework for GDPR Based on Private Blockchain

Cited by 7 publications

References 34 publications

An Optimized and Scalable Blockchain-Based Distributed Learning Platform for Consumer IoT

An Optimized and Scalable Blockchain-Based Distributed Learning Platform for Consumer IoT

Advances in Information Security and Privacy

Formal models for consent management in healthcare software system development

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