arrow
Return

Quantum secure privacy-preserving array equality comparison protocol

delete2026-01-01
delete0
PRE
AI
M
Min Hou
Y
Yue Wu
S
Shibin Zhang *
DOI:10.3934/math.2026311delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
This paper presents a quantum protocol for the secure privacy-preserving equality comparison of private arrays. The design overcomes the scalability limitations of conventional quantum private comparison schemes, which are typically restricted to single integers. In the proposed approach, each array element is encoded into the amplitude of a single-photon state. Participants then encrypt the received states via local rotation operations with privately chosen random angles. A circular transmission mode is employed, whereby the encrypted states are sequentially exchanged among all parties, including a semi-honest third party. The TP performs single-particle measurements to determine the equality of the arrays and broadcasts the result simultaneously to ensure fairness. The protocol's functionality and practicality were validated through quantum circuit simulations on the IBM Qiskit platform. Security analysis confirms its robustness against both external eavesdropping attacks and internal privacy threats from honest-but-curious participants. By utilizing single-photon encoding, rotation-based encryption, and single-particle measurement, the protocol achieves enhanced scalability and practical feasibility for multi-element comparisons, advancing beyond the scope of existing single-integer comparison schemes.
Keywords:
privacy-preserving equality array comparison
quantum private comparison
circular

Journal

A
AIMS Mathematics
IF:
1.8
Papers:
474
Citations:
0

Organization

U
university of electronic science & technology of china
Scholars:
3.2K
Papers: 970
Citations: 0
S
sichuan university
Scholars:
12.1W
Papers: 7.8W
Citations: 100
Cited Papers

Cited Papers

errShare
errSave
err
IF0
err
err0
PREAI
err
errShare
errSave
Secure Multi-Party Computation for Machine Learning: A Survey
err2024-01-01
err10
errOAAI
errZhou, Ian; Tofigh, Farzad; Piccardi, Massimo; Abolhasan, Mehran; Franklin, Daniel; Lipman, Justin
errShare
errSave
errShare
errSave
errShare
errSave
One-step quantum secure direct communication
err2022-02-01
err253
PREAI
errSheng, Yu-Bo; Zhou, Lan; Long, Gui-Lu
errShare
errSave
errShare
errSave
researcher View more