Introduction and ‘Checklist’
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Data centralization for multi-site collaborations is difficult to scale, especially globally, due to concerns about privacy, data ownership, intellectual property, technical challenges, and varying regulatory policies.
“This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [13]
FL offers security features which create an excellent alternative for designing smart and secured IoT devices, especially in light of increasingly severe data privacy protection regulations such as the GDPR.
“FL offers security features which creates an excellent alternative for designing smart and secured IoT devices, especially in light of increasingly severe data privacy protection regulations such as the General Data Protection Regulation (GDPR) [95, 96].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [95]
Cited by
Sorted by citations
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69250-1 · Cited by 1,646 · see citing works
Federated learning enables big data for rare cancer boundary detection
Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33407-5 · Cited by 394 · see citing works
Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399 · see citing works
Rethinking technology innovation for mental health: framework for multi-sectoral collaboration
Suh, J., Pendse, S. R., Lewis, R., Howe, E., Saha, K., Okoli, E., Amores, J., Ramos, G., Shen, J., Borghouts, J., et al. (2024) · Nature Mental Health · doi:10.1038/s44220-024-00232-2 · Cited by 8 · see citing works
- Authors
- Voigt, P., & von dem Bussche, A.
- Venue
- The EU General Data Protection Regulation (GDPR)
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 1-7
- OpenAlex
- W2744790096
- Cited by
- 264
- References
- 0
- Citations by year
- 2026: 56 · 2025: 68 · 2024: 63 · 2023: 29 · 2022: 18 · 2021: 12 · 2020: 13 · 2019: 3 · 2018: 2
- FWCI
- 36.46
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Law › Government, Law, and Information Management
- Social Sciences › Law › European and International Contract Law
- Keywords
- General Data Protection Regulation, EU Data Protection Directive, data protection obligations, personal data protection
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.
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