The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys
Open accessAbstract
,
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.
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
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
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
Self-citationApproximate Homomorphic Encryption with Reduced Approximation Error
Kim, A., Papadimitriou, A., & Polyakov, Y. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-95312-6_6 · Cited by 71
Cited by
Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network
Lee, J.-W., Kang, H., Lee, Y., Choi, W., Eom, J., Deryabin, M., Lee, E., Lee, J., Yoo, D., Kim, Y.-S., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3159694 · Cited by 414
Survey on Fully Homomorphic Encryption, Theory, and Applications
Marcolla, C., Sucasas, V., Manzano, M., Bassoli, R., Fitzek, F. H. P., & Aaraj, N. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3205665 · Cited by 323
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
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
Self-citationApproximate Homomorphic Encryption with Reduced Approximation Error
Kim, A., Papadimitriou, A., & Polyakov, Y. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-95312-6_6 · Cited by 71
Metadata
- Authors
- Bossuat, J.-P., Mouchet, C., Troncoso-Pastoriza, J., & Hubaux, J.-P.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 587-617
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3094696138
- Cited by
- 176
- References
- 3
- FWCI
- 10.12
- Type
- conference paper
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys. https://app.cochat.ai/papers/10.1007/978-3-030-77870-5_21
- The paper
- Bossuat, J.-P., Mouchet, C., Troncoso-Pastoriza, J., & Hubaux, J.-P. (2021). Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-77870-5_21
Data sources
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.
