Huanguo Zhang

2papers

2 Papers

CRNov 19, 2019
Greenberger-Horne-Zeilinger-based quantum private comparison protocol with bit-flipping

Zhaoxu Ji, Peiru Fan, Huanguo Zhang et al.

By introducing a semi-honest third party (TP), we propose in this paper a novel QPC protocol using (n+1)- qubit (n \ge 2) Greenberger-Horne-Zeilinger (GHZ) states as information carriers. The parameter n not only determines the number of qubits contained in a GHZ state, but also determines the probability that TP can successfully steal the participants' data and the qubit efficiency. In the proposed protocol, we do not employ any other quantum technologies (e.g., entanglement swapping and unitary operation) except necessary technologies such as preparing quantum states and quantum measurements, which can reduce the need for quantum devices. We use the keys generated by quantum key distribution and bit-flipping for privacy protection, and decoy photons for eavesdropping checking, making both external and internal attacks invalid. Specifically, for external attacks, we take several well-known attack means (e.g., the intercept-resend attack and the measurement-resend attack) as examples to show that the attackers outside the protocol can not steal the participants' data successfully, in which we provide the security proof of the protocol against the entanglement-measurement attack. For internal attacks, we show that TP cannot steal the participants' data and the participants cannot steal each other's data. We also show that the existing attack means against QPC protocols are invalid for our protocol.

CVFeb 24, 2014
A Novel Face Recognition Method using Nearest Line Projection

Huanguo Zhang, Sha Lv, Wei Li et al.

Face recognition is a popular application of pat- tern recognition methods, and it faces challenging problems including illumination, expression, and pose. The most popular way is to learn the subspaces of the face images so that it could be project to another discriminant space where images of different persons can be separated. In this paper, a nearest line projection algorithm is developed to represent the face images for face recognition. Instead of projecting an image to its nearest image, we try to project it to its nearest line spanned by two different face images. The subspaces are learned so that each face image to its nearest line is minimized. We evaluated the proposed algorithm on some benchmark face image database, and also compared it to some other image projection algorithms. The experiment results showed that the proposed algorithm outperforms other ones.