Homomorphic Encryption Standard

Martin Albrecht (), Melissa Chase (), Hao Chen (), Jintai Ding (), Shafi Goldwasser (), Sergey Gorbunov (), Shai Halevi (), Jeffrey Hoffstein (), Kim Laine (), Kristin Lauter (), Satya Lokam (), Daniele Micciancio (), Dustin Moody (), Travis Morrison (), Amit Sahai () and Vinod Vaikuntanathan ()
Additional contact information
Martin Albrecht: Facebook
Melissa Chase: Microsoft Research, Cryptography and Privacy Research Group
Hao Chen: Facebook
Jintai Ding: Facebook
Shafi Goldwasser: Facebook
Sergey Gorbunov: Facebook
Shai Halevi: Facebook
Jeffrey Hoffstein: Facebook
Kim Laine: Microsoft Research, Cryptography and Privacy Research Group
Kristin Lauter: Microsoft Research, Cryptography and Privacy Research Group
Satya Lokam: Facebook
Daniele Micciancio: Facebook
Dustin Moody: National Institute of Standards and Technology, Computer Security Division
Travis Morrison: Virginia Tech University, Mathematics
Amit Sahai: UCLA, Computer Science
Vinod Vaikuntanathan: Massachusetts Institute of Technology, Electrical Engineering and Computer Science

A chapter in Protecting Privacy through Homomorphic Encryption, 2021, pp 31-62 from Springer

Abstract: Abstract We met as a group during the Homomorphic Encryption Standardization Workshop on July 13–14, 2017, hosted at Microsoft Research in Redmond, and again during the second workshop on March 15–16, 2018 in MIT. Researchers from around the world represented government, industry, and academia. There are several research groups around the world who have made libraries for general-purpose homomorphic encryption available for applications and general-purpose use. Some examples include [40–46,47]. Most general-purpose libraries for homomorphic encryption implement schemes that are based on the ring learning-with-error (RLWE) problem, and many of them displayed common choices for the underlying rings, error distributions, and other parameters.

Date: 2021
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DOI: 10.1007/978-3-030-77287-1_2

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