The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Federated Diabetes Mellitus Analysis via Homomorphic Encryption
Open accessAbstract
Abstract Diabetes is one of the most extensive chronic diseases in the world. Most patients suffer from the disease and its complications for a long time due to the lack of accurate and standardized treatment at early stage. Therefore, by analyzing diabetes data and establishing relevant predictive models, it is very meaningful to give reasonable health advice to high-risk groups. The establishment of an accurate prediction model requires a large number of data sources as support, and multiple medical institutions participate in data contribution and collaborative learning. Federated learning provides a secure general architecture for distributed collaborative learning. However, in the process of federated learning, the data of participants is still subject to the risk of security attacks or indirect information leakage. For example, when a participant uploads the local model parameters to an honest but curious cloud server, the cloud server can obtain relevant information from the participant. In order to solve this problem, this paper proposes a federated forest algorithm based on homomorphic encryption to strengthen the protection of the data privacy of the participants while ensuring that the accuracy of data analysis does not decrease. Analysis proves that our algorithm has good performance in privacy protection and prediction accuracy.
Overview
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
Verification
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?
In the citing paper
“Homomorphic encryption is used to provide privacy while training predictive models on diabetes data using the FL system.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [34]
In the citing paper
“This approach is very effective for FL privacy preservation as most of the operations required by the FL systems like aggregation, model updates, and gradient calculations can be performed without accessing the raw data [34,35,36,37,38].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [34]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Federated Learning: Strategies for Improving Communication Efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016) · arXiv · doi:10.48550/arxiv.1610.02527 · Cited by 1,563
Machine learning and data mining methods in diabetes research
Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.12.005 · Cited by 1,445
Yang (2019) · doi:10.1145/3339474 · Cited by 632
Strack, B., DeShazo, J. P., Gennings, C., Olmo, J. L., Ventura, S., Cios, K. J., & Clore, J. N. (2014) · BioMed Research International · doi:10.1155/2014/781670 · Cited by 380
Multi-View Fusion with Extreme Learning Machine for Clustering
Zhang, Y., Wu, J. X., Zhou, C., Cai, Z., Yang, J. Y., & Yu, P. S. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3340268 · Cited by 71
Differential private random forest
Patil, A., & Singh, S. (2014) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2014.6968348 · Cited by 58
Decision support systems for predicting diabetes mellitus — A Review
Vijayan, V. V., & Anjali, C. (2015) · Global Conference on Communication Technologies · doi:10.1109/gcct.2015.7342631 · Cited by 36
An Intelligible Risk Stratification Model Based on Pairwise and Size Constrained Kmeans
Han, L., Luo, S., Wang, H., Pan, L., Ma, X., & Zhang, T. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2633403 · Cited by 23
Design and Implementation of Diabetes Risk Assessment Model Based on Mobile Things
Gang, S., Shanshan, L., & Ding, Y. (2015) · International Conference on Information Technology in Medicine and Education · doi:10.1109/itme.2015.169 · Cited by 7
Research works printed but not linked: 7
- Luo, 2014, 748, 10.1109/ICDMW.2014:177 · DOI
- Wei, 2016, Analysis for Risk Factors of Type 2 Diabetes Mellitus based on FP-growth Algorithm[J], China Medical Equipment, 13, 45
- Guo, 2014, Application of Artificial Neural Network to Predict Individual Risk of Type 2 Diabetes Mellitus[J], Journal of Zhengzhou University, 49, 180
- Xidan, 2017, Application of Deep Learning Model in Type 2 Diabetes Risk Prediction[J], Journal of Clinical Medicine, 4, 16460
- Rivest, 1978, On databanks and privacy homomorphism[J], Foundations of Secure Computation, 4, 169
- Gentry, 2009, Fully homomorphic encryption using ideal lattices[C], STOC, 9, 169
- Goldreich, 2004, Foundations of Cryptography: Basic Applications [J], Journal of the Acm, 10, 359
Cited by
Sorted by citations
A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems
Gu, X., Sabrina, F., Fan, Z., & Sohail, S. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20156539 · Cited by 139
Recent methodological advances in federated learning for healthcare
Zhang, F., Kreuter, D., Chen, Y., Dittmer, S., Tull, S., Shadbahr, T., Schut, M. C., Asselbergs, F. W., Kar, S., Sivapalaratnam, S., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101006 · Cited by 152
A review of federated learning technology and its research progress in healthcare applications
Ma, Z., Ruhaiyem, N. I. R., Zhang, M., Musa, K. I., Hanis, T. M., Xiao, T., Hua, D., & Li, H. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06627-7 · Cited by 11
An Innovative Approach Towards Efficient Diabetes Mellitus Prediction Using Federated Forest
Madhu, G., De, A., & Pal, G. (2024) · IEEE Silchar Subsection Conference (SILCON) · doi:10.1109/silcon63976.2024.10910400 · Cited by 2
Comprehensive review on federated learning with cryptographic protections
Yadav, G. B., & Bagade, J. (2025) · AIP conference proceedings · doi:10.1063/5.0292103
Metadata
- Authors
- Xie, Y., Li, P., Zhu, X., & Wu, Q.
- Affiliations
- Nanjing University of Posts and Telecommunications (CN)
- Jinling Institute of Technology (CN)
- ORCID
- Venue
- Journal of Physics Conference Series
- Publisher
- IOP Publishing
- Published
- 2020-11-01
- Volume
- 1684
- Issue
- 1
- Pages
- 012033
- ISSN
- 1742-6588 (ISSN-L)
- 1742-6596
- OpenAlex
- W3107707972
- Cited by
- 5
- References
- 8
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 1
- FWCI
- 0.10
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Journal of Physics Conference Series (publishedVersion, cc-by)
- Funders
- Government of Jiangsu Province
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- diabetes mellitus, homomorphic encryption, federated learning, privacy-preserving machine learning, diabetes prediction, data privacy, honest-but-curious server, cooperative learning
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
