Secure and Efficient Two-party Quantum Scalar Product Protocol With
Application to Privacy-preserving Matrix Multiplication
- URL: http://arxiv.org/abs/2309.15856v1
- Date: Sat, 23 Sep 2023 14:33:46 GMT
- Title: Secure and Efficient Two-party Quantum Scalar Product Protocol With
Application to Privacy-preserving Matrix Multiplication
- Authors: Wen-Jie Liu, Zi-Xian Li
- Abstract summary: Two-party quantum scalar product (S2SP) is a promising research area within secure multiparty computation (SMC)
Existing quantum S2SP protocols are not efficient enough, and the complexity is usually close to exponential level.
In this paper, a novel secure two-party quantum scalar product (S2QSP) protocol based on Fourier states is proposed to achieve higher efficiency.
- Score: 2.770988618353868
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Secure two-party scalar product (S2SP) is a promising research area within
secure multiparty computation (SMC), which can solve a range of SMC problems,
such as intrusion detection, data analysis, and geometric computations.
However, existing quantum S2SP protocols are not efficient enough, and the
complexity is usually close to exponential level. In this paper, a novel secure
two-party quantum scalar product (S2QSP) protocol based on Fourier entangled
states is proposed to achieve higher efficiency. Firstly, the definition of
unconditional security under malicious models is given. And then, an honesty
verification method called Entanglement Bondage is proposed, which is used in
conjunction with the modular summation gate to resist malicious attacks. The
property of Fourier entangled states is used to calculate the scalar product
with polynomial complexity. The unconditional security of our protocol is
proved, which guarantees the privacy of all parties. In addition, we design a
privacy-preserving quantum matrix multiplication protocol based on S2QSP
protocol. By transforming matrix multiplication into a series of scalar product
processes, the product of two private matrices is calculated without revealing
any privacy. Finally, we show our protocol's feasibility in IBM Qiskit
simulator.
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