IDMoB: IoT Data Marketplace on Blockchain

Özyılmaz, Kazım Rıfat; Dog̃an, Mehmet Ug̃ur; Yurdakul, Arda

doi:10.1109/cvcbt.2018.00007

Cited by 104 publications

(73 citation statements)

References 3 publications

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“…For example, DLT-based data markets provide the ability to create economic incentives, which could not only stimulate the democratization of access to extant, high-quality AI training data (i.e., addressing the training data availability tension) but as well encourage greater participation by the general public to drive the generation of new, more diverse data sets (i.e., addressing the training data bias tension). However, despite first technical solutions being developed by researchers from the IS, computer science, and related disciplines (Ozercan et al 2018;Özyilmaz et al 2018;Xiong and Xiong 2019;Zhao et al 2019), the question of how to effectively design token economies (e.g., to democratize data access or to encourage the generation of more diverse data sets) remains a focal theme of contemporary DLT research. Adding to this, several researchers have raised concerns over the potential consequences of over-emphasizing economic incentives for the sharing of personal data because they could especially motivate those in need to share their data and without making Table 4 Fruitful avenues of future research on the DLT-based realization of TAI, related tensions, and exemplary research questions…”

Section: Dlt-based Data Marketsmentioning

confidence: 99%

Trustworthy artificial intelligence

2020

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Artificial intelligence (AI) brings forth many opportunities to contribute to the wellbeing of individuals and the advancement of economies and societies, but also a variety of novel ethical, legal, social, and technological challenges. Trustworthy AI (TAI) bases on the idea that trust builds the foundation of societies, economies, and sustainable development, and that individuals, organizations, and societies will therefore only ever be able to realize the full potential of AI, if trust can be established in its development, deployment, and use. With this article we aim to introduce the concept of TAI and its five foundational principles (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. We further draw on these five principles to develop a data-driven research framework for TAI and demonstrate its utility by delineating fruitful avenues for future research, particularly with regard to the distributed ledger technology-based realization of TAI.

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Section: Dlt-based Data Marketsmentioning

confidence: 99%

Trustworthy artificial intelligence

2020

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“…To incentivize participation with high quality data, Harris and Waggoner [73] propose staking mechanisms in which malicious participants sharing spam models lose their stake. Such systems can be applied in the Internet of Things industry in general [72].…”

Section: ) Smart Contractsmentioning

confidence: 99%

On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda

et al. 2020

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Developments in Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) currently lead to lively debates in academia and practice. AI processes data to perform tasks that were previously thought possible only for humans. DLT has the potential to create consensus over data among a group of participants in uncertain environments. In recent research, both technologies are used in similar and even the same systems. Examples include the design of secure distributed ledgers or the creation of allied learning systems distributed across multiple nodes. This can lead to technological convergence, which in the past, has paved the way for major innovations in information technology. Previous work highlights several potential benefits of the convergence of AI and DLT but only provides a limited theoretical framework to describe upcoming real-world integration cases of both technologies. We aim to contribute by conducting a systematic literature review on previous work and providing rigorously derived future research opportunities. This work helps researchers active in AI or DLT to overcome current limitations in their field, and practitioners to develop systems along with the convergence of both technologies.

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“…the proposal of a blockchain based architecture where data is the main focus. In the case of data sharing restricted by payment, a basis for a marketplace is needed, such as the systems presented in [23], [24] that are backed by blockchains for IoT infrastructures and smart cities.…”

Section: B Related Workmentioning

confidence: 99%

A Distributed Ledger Based Infrastructure for Smart Transportation System and Social Good

Zichichi

Ferretti

D’Angelo

2020

2020 IEEE 17th Annual Consumer Communications &Amp; Networking Conference (CCNC)

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This paper presents a system architecture to promote the development of smart transportation systems. Thanks to the use of distributed ledgers and related technologies, it is possible to create, store and share data generated by users through their sensors, while moving. In particular, IOTA and IPFS are used to store and certify data (and their related metadata) coming from sensors or by the users themselves. Ethereum is exploited as the smart contract platform that coordinates the data sharing and provisioning. The necessary privacy guarantees are provided by the usage of Zero Knowledge Proof. We show some results obtained from some use case scenarios that demonstrate how such technologies can be integrated to build novel smart services and to promote social good in user mobility.

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IDMoB: IoT Data Marketplace on Blockchain

Cited by 104 publications

References 3 publications

Trustworthy artificial intelligence

Trustworthy artificial intelligence

On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda

A Distributed Ledger Based Infrastructure for Smart Transportation System and Social Good

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