Edge ai and blockchain for privacy-critical and data-sensitive applications
Open accessAbstract
The edge and fog computing paradigms enable more responsive and smarter systems without relying on cloud servers for data processing and storage. This reduces network load as well as latency. Nonetheless, the addition of new layers in the network architecture increases the number of security vulnerabilities. In privacy-critical systems, the appearance of new vulnerabilities is more significant. To cope with this issue, we propose and implement an Ethereum Blockchain based architecture with edge artificial intelligence to analyze data at the edge of the network and keep track of the parties that access the results of the analysis, which are stored in distributed databases.
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?
Real-time data processing at the edge is used for applications where processing speed is critical, such as video and audio streaming, online gaming, and financial trading.
“For example, real-time data processing approach at the edge for applications where the processing speed is critical, such as video and audio streaming, online gaming, and financial trading [80].”
From Edge AI: A survey (2023) · cites this paper as [80]
The Abstract says edge processing makes systems more responsive and reduces latency, and Section IV reports processing times for an edge-based ECG application. The paper does not identify video or audio streaming, online gaming, or financial trading as applications; the claim extends its point beyond the setting studied. Page numbers were not available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A., et al. (2018) · Oncotarget · doi:10.18632/oncotarget.22345 · Cited by 471 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- T. K. Mackey et al. 'fit-for-purpose?'-challenges and opportunities for applications of blockchain technology in the future of healthcare. BMC medicine, 17(1):68, 2019.
Cited by
Sorted by citations
Singh, R., & Gill, S. S. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.02.004 · Cited by 572 · see citing works
Blockchain for Edge of Things: Applications, Opportunities, and Challenges
Gadekallu, T. R., Pham, Q.-V., Nguyen, D. C., Maddikunta, P. K. R., Deepa, N., Prabadevi, B., Pathirana, P. N., Zhao, J., & Hwang, W.-J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3119639 · Cited by 217 · see citing works
Federated Learning in Robotic and Autonomous Systems
Xianjia, Y., Queralta, J. P., Heikkonen, J., & Westerlund, T. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.07.041 · Cited by 79 · see citing works
Self-citationKamruzzaman, M. M., Alrashdi, I., & Alqazzaz, A. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/2950699 · Cited by 68 · see citing works
Retracted
- Authors
- Nawaz, A., Gia, T. N., Queralta, J. P., & Westerlund, T.
- Affiliations
- University of Turku (FI)
- Venue
- 2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)
- Publisher
- IEEE
- Published
- 2019-11
- Pages
- 1-2
- OpenAlex
- W3008110138
- Cited by
- 39
- References
- 1
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 8 · 2022: 8 · 2021: 9 · 2020: 5 · 2019: 1
- FWCI
- 4.58
- Type
- conference paper
- Language
- en
- Open access
- green
- www.utupub.fi
- Locations
- International Conference on Mobile Computing and Ubiquitous Network (ICMU) (publishedVersion)
- UTUPub (University of Turku) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- edge AI, blockchain, Ethereum, fog computing, distributed databases, network security vulnerabilities, edge computing architecture
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
