A Blockchain-based Decentralized Data Sharing Infrastructure for Off-grid Networking
Open accessAbstract
Off-grid networks are recently emerging as a solution to connect the unconnected or provide alternative services to networks of possibly untrusted participants. The systems currently used, however, exhibit limitations due to their centralized nature and thus prove inadequate to secure trust. Blockchain technology can be the tool that will enable trust and transparency in such networks. In this paper, we introduce a platform for secure and privacy-respecting decentralized data sharing among untrusted participants in off-grid networks. The proposed architecture realizes this goal via the integration of existing blockchain frameworks (Hyperledger Fabric, Indy, Aries) with an off-grid network device and a distributed file system. We evaluate the proposed platform through experiments and show results for its throughput and latency, which indicate its adequate performance for supporting off-grid decentralized applications.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Federated Learning and blockchain have emerged as promising solutions to address challenges of decentralized data-sharing.
“Several decentralized data-sharing technologies and techniques, such as Federated Learning (FL) [10] and blockchain [11], have emerged as promising solutions to address these challenges.”
From Federated learning meets blockchain in decentralized data-sharing: Healthcare use case (2024) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 8 · Cited by 16
Research works printed but not linked: 1
- C. Gorenflo, S. Lee, L. Golab, and S. Keshav, "FastFabric: Scaling hyperledger fabric to 20,000 transactions per second," in International Conference on Blockchain and Cryptocurrency (ICBC). IEEE, 2019.
- Authors
- Niavis, H., Papadis, N., Reddy, V., Rao, H., & Tassiulas, L.
- Affiliations
- Yale University (US)
- Venue
- 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)
- Publisher
- IEEE
- Published
- 2020-05
- Pages
- 1-5
- OpenAlex
- W3034199244
- Cited by
- 16
- References
- 8
- Citations by year
- 2025: 3 · 2024: 4 · 2023: 1 · 2022: 5 · 2021: 2 · 2018: 1
- FWCI
- 1.52
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1932220)
- Tata Consultancy Services
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- blockchain, decentralized data sharing, Hyperledger Fabric, Indies, ARIES, distributed file systems, privacy, trust, decentralized applications, throughput and latency
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
