Linearly-Homomorphic Signatures and Scalable Mix-Nets

Chloé Hébant, Duong Hieu Phan, and David Pointcheval

Abstract

Anonymity is a primary ingredient for our digital life. Several tools have been designed to address it such as, for authentication, blind signatures, group signatures or anonymous credentials and, for confidentiality, randomizable encryption or mix-nets. When it comes to complex electronic voting schemes, random shuffling of ciphertexts with mix-nets is the only known tool. However, it requires huge and complex zero-knowledge proofs to guarantee the actual permutation of the initial ciphertexts. In this paper, we propose a new approach for proving correct shuffling: the mix-servers can simply randomize individual ballots, which means the ciphertexts, the signatures, and the verification keys, with an additional global proof of constant size, and the output will be publicly verifiable. The computational complexity for the mix-servers is linear in the number of ciphertexts. Verification is also linear in the number of ciphertexts, independently of the number of rounds of mixing. This leads to the most efficient technique, that is highly scalable. Our constructions make use of linearly-homomorphic signatures, with new features, that are of independent interest.

Available format(s)
Category
Cryptographic protocols
Publication info
DOI
10.1007/978-3-030-45388-6_21
Keywords
Anonymityrandom shufflinglinearly-homomorphic signatures
Contact author(s)
chloe hebant @ ens fr
History
2020-07-22: last of 2 revisions
See all versions
Short URL
https://ia.cr/2019/547

CC BY

BibTeX

@misc{cryptoeprint:2019/547,
author = {Chloé Hébant and Duong Hieu Phan and David Pointcheval},
title = {Linearly-Homomorphic Signatures and Scalable Mix-Nets},
howpublished = {Cryptology ePrint Archive, Paper 2019/547},
year = {2019},
doi = {10.1007/978-3-030-45388-6_21},
note = {\url{https://eprint.iacr.org/2019/547}},
url = {https://eprint.iacr.org/2019/547}
}

Note: In order to protect the privacy of readers, eprint.iacr.org does not use cookies or embedded third party content.