Secure multiparty computation for maximum and minimum values based on quantum homomorphic encryption

Secure multiparty computation is a basic cryptographic primitive that has many important applications in privacy preservation. In this work, we propose a new secure multiparty computation protocol for the maximum and minimum values based on quantum homomorphic encryption. Owing to the speciality of quantum homomorphic encryption, participants can delegate a server to compute the maximum and minimum values of their private data. Furthermore, both calculations are performed on the encrypted data, and therefore the privacy of their respective private data can be perfectly guaranteed.

Authors

Publication Details

Journal
Optics Express
Published
2025-03-18
DOI
https://doi.org/10.1364/oe.555634
Citations
2
Primary Topic
Cryptography and Data Security
Type
article
Field-Weighted Citation Impact
4.96
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Secure multiparty computation for maximum and minimum values based on quantum homomorphic encryption

Tian‐Yin Wang, Shuang Li, Xiao‐Qiu Cai
2 citations
Optics Express
Cryptography and Data Security
4.96
article

Secure multiparty computation for maximum and minimum values based on quantum homomorphic encryption

Tian‐Yin Wang, Shuang Li, Xiao‐Qiu Cai
article en
2 citations

Abstract

Secure multiparty computation is a basic cryptographic primitive that has many important applications in privacy preservation. In this work, we propose a new secure multiparty computation protocol for the maximum and minimum values based on quantum homomorphic encryption. Owing to the speciality of quantum homomorphic encryption, participants can delegate a server to compute the maximum and minimum values of their private data. Furthermore, both calculations are performed on the encrypted data, and therefore the privacy of their respective private data can be perfectly guaranteed.

Optics ExpressVol. 33(7)
Openalex Percentile: Top 6%
Cryptography and Data Security
4.96
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.