ML Confidential: Machine Learning on Encrypted Data
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
The ConfML protocol has been proposed for data owners to adhere to when they share training data with an ML service, and it protects the privacy of training data during the training process.
“Towards that, Confidential Machine Learning, a.k.a.ConfML protocol, has been proposed that the data owners must adhere to when they share training data with an ML service [107].This protocol protects the privacy of training data during the training process.”
From Confidential Computing Across Edge‐To‐Cloud for Machine Learning: A Survey Study (2025) · cites this paper as [107]
ConfML eliminates the vulnerability of data exposure during training by protecting the privacy of training data during the training process.
“ConfML eliminates this vulnerability by protecting the privacy of training data during the training process [107].”
From Confidential Computing Across Edge‐To‐Cloud for Machine Learning: A Survey Study (2025) · cites this paper as [107]
Cited by
Sorted by citations
Private predictive analysis on encrypted medical data
Bos, J. W., Lauter, K., & Naehrig, M. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.04.003 · Cited by 283 · see citing works
A Systematic Review of Homomorphic Encryption and Its Contributions in Healthcare Industry
Munjal, K., & Bhatia, R. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00756-z · Cited by 269 · see citing works
When Machine Learning Meets Privacy in 6G: A Survey
Sun, Y., Liu, J., Wang, J., Cao, Y., & Kato, N. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3011561 · Cited by 211 · see citing works
Privacy-preserving distributed linear regression on high-dimensional data
Gascón, A., Schoppmann, P., Balle, B., Raykova, M., Doerner, J., Zahur, S., & Evans, D. (2017) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2017-0053 · Cited by 193 · see citing works
POSEIDON: Privacy-Preserving Federated Neural Network Learning
Sav, S., Pyrgelis, A., Troncoso-Pastoriza, J. R., Froelicher, D., Bossuat, J.-P., Sousa, J. S., & Hubaux, J.-P. (2021) · Proceedings 2021 Network and Distributed System Security Symposium · doi:10.14722/ndss.2021.24119 · Cited by 162 · see citing works
Privacy in Deep Learning: A Survey
Mireshghallah, F., Taram, M., Vepakomma, P., Singh, A., Raskar, R., & Esmaeilzadeh, H. (2020) · arXiv · doi:10.48550/arxiv.2004.12254 · Cited by 106 · see citing works
Open Problems in Cooperative AI
Dafoe, A., Hughes, E., Bachrach, Y., Collins, T., McKee, K. R., Leibo, J. Z., Larson, K., & Graepel, T. (2020) · arXiv · doi:10.48550/arxiv.2012.08630 · Cited by 93 · see citing works
Self-citationConfidential Computing Across Edge‐To‐Cloud for Machine Learning: A Survey Study
Zobaed, S., & Amini Salehi, M. (2025) · Software Practice and Experience · doi:10.1002/spe.3398 · Cited by 21 · see citing works
- Authors
- Graepel, T., Lauter, K., & Naehrig, M.
- Affiliations
- Microsoft Research (United Kingdom) (GB)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Pages
- 1-21
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W133884053
- Cited by
- 423
- References
- 0
- Citations by year
- 2026: 5 · 2025: 20 · 2024: 22 · 2023: 33 · 2022: 29 · 2021: 54 · 2020: 66 · 2019: 60 · 2018: 47 · 2017: 19 · 2016: 27 · 2015: 29 · 2014: 8 · 2013: 4
- FWCI
- 14.64
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- TU/e Research Portal (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- homomorphic encryption, data privacy, polynomial approximation, binary classification, least squares, gradient descent, cloud computing, computational complexity trade-off
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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