EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 76 (2025) ITM Web Conf., 76 (2025) 02010 References
Open Access
Issue
ITM Web Conf.
Volume 76, 2025
Harnessing Innovation for Sustainability in Computing and Engineering Solutions (ICSICE-2025)
Article Number 02010
Number of page(s) 9
Section Blockchain & Cybersecurity
DOI https://doi.org/10.1051/itmconf/20257602010
Published online 25 March 2025
  1. Aharoni, E., Drucker, N., & others. (2022). Advanced HE packing methods with applications to ML. Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (CCS 2022). [Google Scholar]
  2. Baracaldo, N., & Shaul, H. (2022, December 16). Federated Learning meets Homomorphic Encryption. IBM Research Technical Note. [Google Scholar]
  3. Blatt, M., Gusev, A., Polyakov, Y., Rohloff, K., & Vaikuntanathan, V. (2019). Optimized Homomorphic Encryption Solution for Secure Genome-Wide Association Studies. BMC Medical Genomics, 12(Suppl 6), 92. [Google Scholar]
  4. Cousins, D. B., Polyakov, Y., Al Badawi, A., French, M., Schmidt, A., Jacob, A., Reynwar, B., Canida, K., Jaiswal, A., Mathew, C., Gamil, H., Neda, N., Soni, D., Maniatakos, M., Reagen, B., Zhang, N., Franchetti, F., Brinich, P., Johnson, J., Broderick, P., Franusich, M., Zhang, B., Cheng, Z., & Pedram, M. (2023). TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation. arXiv preprint arXiv:2304.05237. [Google Scholar]
  5. Fan, J., & Vercauteren, F. (2012). Somewhat Practical Fully Homomorphic Encryption. IACR Cryptology ePrint Archive, 2012, 144. [Google Scholar]
  6. Fenner, P., & Pyzer-Knapp, E. O. (2020). Privacy-preserving Gaussian process regression: A modular approach to the application of homomorphic encryption. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5574–5581. [Google Scholar]
  7. Flores, R., Kahrobaei, D., & Koberda, T. (2021). Hamiltonicity via cohomology of right-angled Artin groups. Linear Algebra and Its Applications, 610, 1–15. [Google Scholar]
  8. Froelicher, D., Troncoso-Pastoriza, J. R., Raisaro, J. L., Cuendet, M. A., Sousa, J. S., & Hubaux, J. P. (2021). Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic Encryption. Nature Communications, 12, 5910. [Google Scholar]
  9. Gao, Y., Quan, G., Homsi, S., Wen, W., & Wang, L. (2024). Secure and Efficient General Matrix Multiplication On Cloud Using Homomorphic Encryption. arXiv preprint arXiv:2405.02238. [Google Scholar]
  10. Gentry, C., Halevi, S., & Smart, N. P. (2012). Homomorphic Evaluation of the AES Circuit. Advances in Cryptology - CRYPTO 2012, 6917, 850–867. [Google Scholar]
  11. Halevi, S., Polyakov, Y., & Shoup, V. (2019). An Improved RNS Variant of the BFV Homomorphic Encryption Scheme. Proceedings of the 2019 RSA Conference on Topics in Cryptology (CT-RSA 2019), 83–105. [Google Scholar]
  12. Kim, A., Polyakov, Y., & Zucca, V. (2021). Revisiting Homomorphic Encryption Schemes for Finite Fields. IACR Cryptology ePrint Archive, 2021, 204. [Google Scholar]
  13. Liu, Z., Chen, S., Ye, J., Fan, J., Li, H., & Li, X. (2022). DHSA: Efficient Doubly Homomorphic Secure Aggregation for Cross-silo Federated Learning. arXiv preprint arXiv:2208.07189. [Google Scholar]
  14. Lou, Q., Santriaji, M., Yudha, A. W. B., Xue, J., & Solihin, Y. (2023). vFHE: Verifiable Fully Homomorphic Encryption with Blind Hash. arXiv preprint arXiv:2303.08886. [Google Scholar]
  15. Micciancio, D., & Polyakov, Y. (2020). Bootstrapping in FHEW-like Cryptosystems. IACR Cryptology ePrint Archive, 2020, 86. [Google Scholar]
  16. Park, Y., Amarnath, A., & others. (2025). FHENDI: A Near-DRAM Accelerator for Compiler-Generated Fully Homomorphic Encryption Applications. Proceedings of the 2025 IEEE International Symposium on High-Performance Computer Architecture (HPCA 2025). [Google Scholar]
  17. Polyakov, Y., Rohloff, K., Sahu, G., & Vaikuntanathan, V. (2017). Fast Proxy Re-Encryption for Publish/Subscribe Systems. ACM Transactions on Privacy and Security, 20(4), 1–31. [Google Scholar]
  18. Sav, S., Pyrgelis, A., Troncoso-Pastoriza, J. R., Froelicher, D., & Bossuat, J. P. (2021). POSEIDON: Privacy-Preserving Federated Neural Network Learning. Proceedings of the 2021 Network and Distributed System Security Symposium (NDSS 2021). [Google Scholar]
  19. Shokri, R., Theodorakopoulos, G., Le Boudec, J. Y., & Hubaux, J. P. (2011). Quantifying Interdependent Risks in Genomic Privacy. ACM Transactions on Privacy and Security, 15(4), 1–33. [Google Scholar]
  20. Soceanu, O., & Levy, R. (2022, December 8). The ultimate tool for data privacy: Fully homomorphic encryption. IBM Research Technical Note. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.