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ViSDM 1.0: Vision Sovereignty Data Marketplace a Decentralized Platform for Crowdsourcing Data Collection and Trading
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-6895-4503
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-5235-5335
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0003-4814-4428
Bonseyes Community Association, Switzerland.
2023 (English)In: ACM International Conference Proceeding Series, Association for Computing Machinery (ACM), 2023, p. 374-383Conference paper, Published paper (Refereed)
Abstract [en]

The demand for large-scale diverse datasets is rapidly increasing due to the advancements in AI services impacting day-to-day life. However, gathering such massive datasets still remains a critical challenge in the AI service engineering pipeline, especially in the computer vision domain where labeled data is scarce. Rather than isolated data collection, crowdsourcing techniques have shown promising potential to achieve the data collection task in a time and cost-efficient manner. In the existing crowdsourcing marketplaces, the crowd works to fulfill consumer-defined requirements where in the end consumer gains the data ownership and the crowd is compensated with task-based payment. On the contrary, this work proposes a blockchain-based decentralized marketplace named Vision Sovereignty Data Marketplace (ViSDM), in which the crowd works to fulfill global requirements & holds data ownership, the consumers pay a certain data price to perform a computing task (model training/testing), the data price is distributed among the crowd in a one-to-many manner through smart contracts, thus allowing the crowd to gain profit from each consumer transaction occurring on their data. The marketplace is implemented as multiple smart contracts and is evaluated based on blockchain-transaction gas fees for the stakeholder interaction & by running scenarios-based simulations. Furthermore, discussions address the challenges included in maintaining data quality and the future milestones towards deployment. © 2023 Owner/Author.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023. p. 374-383
Keywords [en]
Blockchain, Computer Vision, Crowdsourcing, Data Marketplaces, Smart Contracts, Commerce, Data acquisition, Large dataset, Block-chain, Critical challenges, Data collection, Data ownership, Decentralised, Labeled data, Large-scales, Massive data sets, Services engineering, Smart contract
National Category
Information Systems
Identifiers
URN: urn:nbn:se:bth-25507DOI: 10.1145/3582515.3609556Scopus ID: 2-s2.0-85174318507ISBN: 9798400701160 (print)OAI: oai:DiVA.org:bth-25507DiVA, id: diva2:1808441
Conference
3rd ACM Conference on Information Technology for Social Good, GoodIT 2023Lisbon6 September through 8 September 2023
Part of project
HINTS - Human-Centered Intelligent Realities
Funder
Knowledge Foundation, 20220068EU, Horizon 2020, 101015848Available from: 2023-10-31 Created: 2023-10-31 Last updated: 2025-09-30Bibliographically approved
In thesis
1. Digital Sovereignty for Collaborative AI Engineering
Open this publication in new window or tab >>Digital Sovereignty for Collaborative AI Engineering
2025 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

In Information Systems, implementing digital sovereignty is essential for improving transparency and establishing trust among stakeholders. This need for digital sovereignty is more prevalent in crowdsourcing platforms, where the stakeholders are often unknown to each other. AI marketplaces belong to the category of crowdsourcing information systems, where individuals and organizations collaborate to share various AI artifacts with one another. These marketplaces act as platforms that enable artifact exchange, thus accelerating the AI application development process through a multi-stakeholder approach to collaborative AI engineering. This work, investigates techniques for implementing digital sovereignty to promote collaboration among the stakeholders.

Digital sovereignty thrives by empowering true owners with control and the ability to make independent decisions over their digital footprint. Depending on the application context, the type of control and the decision-makers change accordingly. For governments, digital sovereignty means the ability to manage citizens’ personal data and ensure data residency within a political region. For individual technology users, digital sovereignty refers to the ability to manage and control the interoperability of personal data across similar platforms. Nevertheless, digital sovereignty focuses on transferring control to the true owner by eliminating intermediaries or centralized organizations.

The scope of this work lies in achieving digital sovereignty for marketplace platforms that operate in the context of exchanging data and other AI software artifacts. The Horizon 2020 projects, BonsApps and dAIEdge,  provide a functional crowdsourcing AI marketplace with beta stakeholders, which also serves as a source for gathering requirements and validating concepts. The main contributions of this work are translating digital sovereignty definitions and requirements into the context of collaborative AI, as well as designing and implementing technical solutions to empower stakeholders of the underlying information system with digital sovereignty over their digital assets.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2025. p. 140
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 2025:03
Keywords
Digital Sovereignty, Collaborative AI Engineering, Data Sovereignty, Data Marketplaces, AI Marketplaces
National Category
Computer and Information Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:bth-27712 (URN)978-91-7295-497-7 (ISBN)
Presentation
2025-05-28, J1630, BTH, Valhallavägen 1, Karlskrona, 09:00 (English)
Opponent
Supervisors
Funder
EU, Horizon 2020, 101120726
Available from: 2025-04-11 Created: 2025-04-10 Last updated: 2025-09-30Bibliographically approved

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Daliparthi, Venkata Satya Sai AjayMomen, NurulTutschku, Kurt

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