The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Fast Homomorphic Evaluation of Deep Discretized Neural Networks
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Citations
Cites
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Research works printed but not linked: 2
- Benhamouda, F., Lepoint, T., Mathieu, C., Zhou, H.: Optimization of bootstrapping in circuits. In: Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2017, Philadelphia, PA, USA, pp. 2423–2433. Society for Industrial and Applied Mathematics (2017) · doi:10.1137/1.9781611974782.160
- Lepoint, T., Paillier, P.: On the minimal number of bootstrappings in homomorphic circuits. In: Adams, A.A., Brenner, M., Smith, M. (eds.) FC 2013 Workshops. LNCS, vol. 7862, pp. 189–200. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41320-9_13 · doi:10.1007/978-3-642-41320-9_13
Cited by
Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772
“A common approach has been to adapt ML models and their training procedures to ensure that (over)underflows are controlled, by operating on normalized quantities and relying on careful quantization [172, 14, 182, 77].”
MethodFederated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · arXiv · doi:10.48550/arxiv.1902.04885 · Cited by 600
ABY 3: A Mixed Protocol Framework for Machine Learning
Mohassel, P., & Rindal, P. (2018) · Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3243734.3243760 · Cited by 432
Privacy and Security Issues in Deep Learning: A Survey
Liu, X., Xie, L., Wang, Y., Zou, J., Xiong, J., Ying, Z., & Vasilakos, A. V. (2021) · IEEE Access · doi:10.1109/access.2020.3045078 · Cited by 282
Metadata
- Authors
- Bourse, F., Minelli, M., Minihold, M., & Paillier, P.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 483-512
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2794888826
- Cited by
- 300
- References
- 41
- FWCI
- 13.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
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- Bourse, F., Minelli, M., Minihold, M., & Paillier, P. (2018). Fast Homomorphic Evaluation of Deep Discretized Neural Networks. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-96878-0_17
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
