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Quantum Machine Learning

Quantum Privacy-Preserving Price E-Negotiation

arXiv
Authors: Wen-Jie Liu, Chun-Tang Li, Yu Zheng, Yong Xu, Yin-Song Xu

Year

2023

Paper ID

54494

Status

Preprint

Abstract Read

~2 min

Abstract Words

113

Citations

N/A

Abstract

Privacy-preserving price e-negotiation (3PEN) is an important topic of secure multi-party computation (SMC) in the electronic commerce field, and the key point of its security is to guarantee the privacy of seller's and buyer's prices. In this study, a novel and efficient quantum solution to the 3PEN problem is proposed, where the oracle operation and the qubit comparator are utilized to obtain the comparative results of buyer's and seller's prices, and then quantum counting is executed to summarize the total number of products which meets the trading conditions. Analysis shows that our solution not only guarantees the correctness and the privacy of 3PEN, but also has lower communication complexity than those classical ones.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2023 reference point for readers tracking recent quantum research.
  • Privacy-preserving price e-negotiation (3PEN) is an important topic of secure multi-party computation (SMC) in the electronic commerce field, and the key point of its security...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2309.13668 [2] arXiv https://arxiv.org/abs/2309.13668 [3] Publisher https://arxiv.org/abs/2309.13668

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