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; Vinod Vaikuntanathan

doi:10.1007/978-3-030-77287-1_2

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
, Vinod Vaikuntanathan

^*Corresponding author for this work

Royal Holloway University of London
Microsoft USA
Meta
University of Cincinnati
Simons Institute
University of Waterloo
Algorand Foundation
Brown University
University of California at San Diego
National Institute of Standards and Technology
Virginia Polytechnic Institute and State University
University of California at Los Angeles
Massachusetts Institute of Technology

Research output: Chapter in Book or Report/Conference proceeding › Chapter › peer-review

164 Citations (Scopus)

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.

Original language	English
Title of host publication	Protecting Privacy through Homomorphic Encryption
Publisher	Springer International Publishing
Pages	31-62
Number of pages	32
ISBN (Electronic)	9783030772871
ISBN (Print)	9783030772864
DOIs	https://doi.org/10.1007/978-3-030-77287-1_2
Publication status	Published - 4 Jan 2022
Externally published	Yes

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10.1007/978-3-030-77287-1_2

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Albrecht, M., Chase, M., Chen, H., Ding, J., Goldwasser, S., Gorbunov, S., Halevi, S., Hoffstein, J., Laine, K., Lauter, K., Lokam, S., Micciancio, D., Moody, D., Morrison, T., Sahai, A., & Vaikuntanathan, V. (2022). Homomorphic encryption standard. In Protecting Privacy through Homomorphic Encryption (pp. 31-62). Springer International Publishing. https://doi.org/10.1007/978-3-030-77287-1_2

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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.",

author = "Martin Albrecht and Melissa Chase and Hao Chen and Jintai Ding and Shafi Goldwasser and Sergey Gorbunov and Shai Halevi and Jeffrey Hoffstein and Kim Laine and Kristin Lauter and Satya Lokam and Daniele Micciancio and Dustin Moody and Travis Morrison and Amit Sahai and Vinod Vaikuntanathan",

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Albrecht, M, Chase, M, Chen, H, Ding, J, Goldwasser, S, Gorbunov, S, Halevi, S, Hoffstein, J, Laine, K, Lauter, K, Lokam, S, Micciancio, D, Moody, D, Morrison, T, Sahai, A & Vaikuntanathan, V 2022, Homomorphic encryption standard. in Protecting Privacy through Homomorphic Encryption. Springer International Publishing, pp. 31-62. https://doi.org/10.1007/978-3-030-77287-1_2

TY - CHAP

T1 - Homomorphic encryption standard

AU - Albrecht, Martin

AU - Chase, Melissa

AU - Chen, Hao

AU - Ding, Jintai

AU - Goldwasser, Shafi

AU - Gorbunov, Sergey

AU - Halevi, Shai

AU - Hoffstein, Jeffrey

AU - Laine, Kim

AU - Lauter, Kristin

AU - Lokam, Satya

AU - Micciancio, Daniele

AU - Moody, Dustin

AU - Morrison, Travis

AU - Sahai, Amit

AU - Vaikuntanathan, Vinod

PY - 2022/1/4

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N2 - 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.

AB - 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.

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SN - 9783030772864

SP - 31

EP - 62

BT - Protecting Privacy through Homomorphic Encryption

PB - Springer International Publishing

ER -

Homomorphic encryption standard

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