The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption
Open accessAbstract
Fully Homomorphic Encryption (FHE) allows computing on encrypted data, enabling secure offloading of computation to untrusted servers. Though it provides ideal security, FHE is expensive when executed in software, 4 to 5 orders of magnitude slower than computing on unencrypted data. These overheads are a major barrier to FHE’s widespread adoption.
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Cites
Gradient-based learning applied to document recognition
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Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,395
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Modular multiplication without trial division
Montgomery, P. L. (1985) · Mathematics of Computation · doi:10.1090/s0025-5718-1985-0777282-x · Cited by 2,669
Homomorphic Encryption for Arithmetic of Approximate Numbers
Cheon, J. H., Kim, A., Kim, M., & Song, Y. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-70694-8_15 · Cited by 2,522
On Ideal Lattices and Learning with Errors over Rings
Lyubashevsky, V., Peikert, C., & Regev, O. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-13190-5_1 · Cited by 1,831
Self-citation(Leveled) fully homomorphic encryption without bootstrapping
Brakerski, Z., Gentry, C., & Vaikuntanathan, V. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090262 · Cited by 1,782
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(Leveled) Fully Homomorphic Encryption without Bootstrapping
Brakerski, Z., Gentry, C., & Vaikuntanathan, V. (2014) · ACM Transactions on Computation Theory · doi:10.1145/2633600 · Cited by 1,430
Cited by
OpenFHE: Open-Source Fully Homomorphic Encryption Library
Al Badawi, A., Bates, J., Bergamaschi, F., Cousins, D. B., Erabelli, S., Genise, N., Halevi, S., Hunt, H., Kim, A., Lee, Y., et al. (2022) · Proceedings of the 10th Workshop on Encrypted Computing & Applied Homomorphic Cryptography · doi:10.1145/3560827.3563379 · Cited by 281
FPT: A Fixed-Point Accelerator for Torus Fully Homomorphic Encryption
Van Beirendonck, M., D'Anvers, J.-P., Turan, F., & Verbauwhede, I. (2023) · Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3576915.3623159 · Cited by 38
Metadata
- Authors
- Samardzic, N., Feldmann, A., Krastev, A., Devadas, S., Dreslinski, R., Peikert, C., & Sanchez, D.
- Venue
- MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture
- Publisher
- ACM
- Published
- 2021-10-17
- Pages
- 238-252
- OpenAlex
- W3207326900
- Cited by
- 294
- References
- 52
- FWCI
- 19.13
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Samardzic, N., Feldmann, A., Krastev, A., Devadas, S., Dreslinski, R., Peikert, C., & Sanchez, D. (2021). F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption. MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture. https://doi.org/10.1145/3466752.3480070
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
