A blockchain-based Trust System for the Internet of Things

Pietro, Roberto Di; Salleras, Xavier; Signorini, Matteo; Waisbard, Erez

doi:10.1145/3205977.3205993

Cited by 84 publications

(56 citation statements)

References 13 publications

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“…Blockchain is a valuable concept and a technology that will deeply change the way we foster security and reliability in IoT networks [4,[25][26][27][28] and, in general, in any human-designed ecosystem by providing true dependable data. However, blockchain will not necessarily be a disruption in the way systems are designed and implemented, given the fact that trust may be smoothly "added" to existing legacy systems.…”

Section: Discussionmentioning

confidence: 99%

Enhancing IoT Data Dependability through a Blockchain Mirror Model

Bellini¹,

Bellini

Gherardelli

et al. 2019

Future Internet

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The Internet of Things (IoT) is a remarkable data producer and these data may be used to prevent or detect security vulnerabilities and increase productivity by the adoption of statistical and Artificial Intelligence (AI) techniques. However, these desirable benefits are gained if data from IoT networks are dependable—this is where blockchain comes into play. In fact, through blockchain, critical IoT data may be trusted, i.e., considered valid for any subsequent processing. A simple formal model named “the Mirror Model” is proposed to connect IoT data organized in traditional models to assets of trust in a blockchain. The Mirror Model sets some formal conditions to produce trusted data that remain trusted over time. A possible practical implementation of an application programming interface (API) is proposed, which keeps the data and the trust model in synch. Finally, it is noted that the Mirror Model enforces a top-down approach from reality to implementation instead of going the opposite way as it is now the practice when referring to blockchain and the IoT.

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Section: Discussionmentioning

confidence: 99%

Enhancing IoT Data Dependability through a Blockchain Mirror Model

Bellini¹,

Bellini

Gherardelli

et al. 2019

Future Internet

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“…And the low throughput of Ethereum blockchain cannot satisfy the demands of IIoT system. Di Pietro et al [22] describe a distributed trust model for the IoT that bridges them to create end-to-end trust between IoT devices without any third party, which just simply apply blockchain technology into IoT system and do not present a detailed implementation.…”

Section: Related Workmentioning

confidence: 99%

Towards Secure Industrial IoT: Blockchain System With Credit-Based Consensus Mechanism

Huang

Kong

Chen

et al. 2019

IEEE Trans. Ind. Inf.

466

145

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Industrial Internet of Things (IIoT) plays an indispensable role for Industry 4.0, where people are committed to implement a general, scalable, and secure IIoT system to be adopted across various industries. However, existing IIoT systems are vulnerable to single point of failure and malicious attacks, which cannot provide stable services. Due to the resilience and security promise of blockchain, the idea of combining blockchain and Internet of Things (IoT) gains considerable interest. However, blockchains are power-intensive and low-throughput, which are not suitable for power-constrained IoT devices. To tackle these challenges, we present a blockchain system with credit-based consensus mechanism for IIoT. We propose a credit-based proof-of-work (PoW) mechanism for IoT devices, which can guarantee system security and transaction efficiency simultaneously. In order to protect sensitive data confidentiality, we design a data authority management method to regulate the access to sensor data. In addition, our system is built based on directed acyclic graph-structured blockchains, which is more efficient than the Satoshi-style blockchain in performance. We implement the system on Raspberry Pi, and conduct a case study for the smart factory. Extensive evaluation and analysis results demonstrate that creditbased PoW mechanism and data access control are secure and efficient in IIoT.

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“…Attestable trust anchors on clients can help with usage control of data across devices [48]. Combining reputation anchors with obligation handling [2] or with DLT mechanisms such as obligation chains [41] can make trust more resilient in open systems. Hardware isolation of the access-control functionality from the normal application layers, e.g.…”

Section: Safety Resiliency and Trustmentioning

confidence: 99%

“…Fourth, we briefly relate to work on synthesis or analysis of access-control policies. The high degree of dynamics in IoT networks may benefit from use of event-driven policies [41] and a less static generation and enforcement of access-control policies (e.g. through machine learning) [7].…”

Section: Policy Analysis and Synthesismentioning

confidence: 99%

Owner-Centric Sharing of Physical Resources, Data, and Data-Driven Insights in Digital Ecosystems

Cheung¹,

Huth

Kirk³

et al. 2019

Proceedings of the 24th ACM Symposium on Access Control Models and Technologies

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We are living in an age in which digitization will connect more and more physical assets with IT systems and where IoT endpoints will generate a wealth of valuable data. Companies, individual users, and organizations alike therefore have the need to control their own physical or non-physical assets and data sources. At the same time, they recognize the need for, and opportunity to, share access to such data and digitized physical assets. This paper sets out our technology vision for such sharing ecosystems, reports initial work in that direction, identifies challenges for realizing this vision, and seeks feedback and collaboration from the academic access-control community in that R&D space.

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A blockchain-based Trust System for the Internet of Things

Cited by 84 publications

References 13 publications

Enhancing IoT Data Dependability through a Blockchain Mirror Model

Enhancing IoT Data Dependability through a Blockchain Mirror Model

Towards Secure Industrial IoT: Blockchain System With Credit-Based Consensus Mechanism

Owner-Centric Sharing of Physical Resources, Data, and Data-Driven Insights in Digital Ecosystems

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