Doing Real Work with FHE
Author:
Affiliation:
1. Queen Mary Univ. of London, London, United Kingdom
2. IBM Research, Yorktown Heights, NY, USA
3. NYU, New York, NY, USA
Publisher
ACM Press
Reference27 articles.
1. Yoshinori AONO, Takuya HAYASHI, Le Trieu PHONG, and Lihua WANG. 2016. Privacy-Preserving Logistic Regression with Distributed Data Sources via Homomorphic Encryption. IEICE Transactions on Information and Systems E99.D, 8 (2016), 2079--2089. https://doi.org/10.1587/transinf.2015INP0020
2. Anonymized Authors. 2018. Doing Real Work with FHE: The Case of Logistic Regression. Detailed removed for anonimity.
3. Dan Boneh, Craig Gentry, Shai Halevi, Frank Wang, and David J. Wu. 2013. Private Database Queries Using Somewhat Homomorphic Encryption. In ACNS (Lecture Notes in Computer Science), Vol. 7954. Springer, 102--118.
4. Zvika Brakerski. 2012. Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP. In CRYPTO (Lecture Notes in Computer Science), Reihaneh Safavi-Naini and Ran Canetti (Eds.), Vol. 7417. Springer, 868--886.
5. Zvika Brakerski, Craig Gentry, and Vinod Vaikuntanathan. 2014. (Leveled) Fully Homomorphic Encryption without Bootstrapping. ACM Transactions on Computation Theory 6, 3 (2014), 13. https://doi.org/10.1145/2633600
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