Jason Lin

New quantum private comparison protocol using EPR pairs

Based on EPR pairs, this paper proposes a different quantum private comparison (QPC) protocol enabling two parties to compare the equality of their information without revealing the information content. Due to the use of quantum entanglement of Bell state as well as one-way quantum transmission, the new protocol provides easier implementation as well as better qubit efficiency (near 50%) than the other QPCs. It is secure against Trojan horse attack and other well-known attacks.

New circular quantum secret sharing for remote agents

This study presents a novel circular quantum secret sharing (QSS) protocol based on the controlled-NOT (CNOT) gate for remote agents. A CNOT gate is able to entangle a Bell state and several single photons to form a multi-particle GHZ state. Using this technique, the proposed QSS scheme is designed in purpose to be congenitally free from the Trojan horse attacks. Moreover, for each shared bit among n party, the qubit efficiency has reached

Bell state entanglement swappings over collective noises and their applications on quantum cryptography

{\frac{1}{2n+1}}

Quantifying Tactical Risk: A Framework for Statistical Classification Using MECH

, which is the best among the current circular QSS’s.

Prediction of Mathematical Expression Constraints (ME-Con)

This paper presents a novel quantum private comparison protocol that uses Einstein–Podolsky–Rosen pairs. The proposed protocol allows two parties to secretly compare their information without exposing their actual contents. The technique of entanglement swapping enables the comparison to be achieved without the help of a third party. Moreover, because the proposed protocol employs one-step transmission and decoy photons, it is secure against the various quantum attacks in existence thus far.

QuQn map: Qualitative-Quantitative mapping of scientific papers

This work presents two robust entanglement swappings against two types of collective noises, respectively. The entanglement swapping can be achieved by performing two Bell state measurements on two logical qubits that come from two original logical Bell states, respectively. Two fault tolerant quantum secret sharing (QSS) protocols are further proposed to demonstrate the usefulness of the newly proposed entanglement swappings. The proposed QSS schemes are not only free from Trojan horse attacks but also quite efficient. Moreover, by adopting two Bell state measurements instead of four-qubit joint measurements, the proposed protocols are practical in combating collective noises. The proposed fault tolerant entanglement swapping can also be used to replace the traditional Bell-state entanglement swapping used in various quantum cryptographic protocols to provide robustness in combating collective noises.

Intercept–resend attacks on Chen et al.'s quantum private comparison protocol and the improvements

This paper presents novel computing algorithms to generate tactical risk maps (TRM) based on the MECH (Monitor, Emplacement, and Command/Control in a Halo) model to evaluate locational values for attackers to launch improvised explosive device (IED) vs. direct fire (DF) attacks. Given a study area R, its proximity P can be mapped to explore noticeable characteristics associated with the attack locations. Within the distance constraints of the Halo, a simple optimization formula is proposed to support flexible representations of risk preferences of the attackers in ranking of locations for the M, C and E functions across R. Several case studies on major corridors find a significant number of attack locations were near or at local maxima of the measurement route exposure. It was found that IED sites tend to have good visibility and more uniform line-of-sight (LOS) distances. On the other hand, most DF locations are near the boundary of the viewshed suggesting careful selection of the sites to provide cover in the attack.

An enhancement on Shi et al.'s multiparty quantum secret sharing protocol

This paper presents a statistical classification framework for classification and prediction of asymmetric conflict (AC) locations. Different data normalization and feature reduction methods are coupled with supervised machine learning training algorithms to train classifiers. A set of 77 features derived from the MECH Model (Monitor, Emplacement, and Control in a Halo) were used to train the classifiers. The framework has been implemented and tested on real-world improvised explosive device and direct fire data collected from the conflict in Afghanistan in 2011-2012. Empirical results show that the classifiers achieve high accuracy, with human behavior-related features (visibility and population) exhibiting the most significant statistical differences. Performance of the classifiers is insensitive to the training algorithms. While performance is positively correlated to the training data size as expected, good performance is achieved with a fairly small amount of training data. Experiments based on cross-region training and prediction also show that classifiers are region dependent.