arXiv subjects

Mingyang Zhong

Publications and source records attributed to Mingyang Zhong.

4 recordsLinked to original sources

arXiv2026

Quantifying the Upper Limit of Backflash Attack in Quantum Key Distribution

Quantum key distribution (QKD) provides information-theoretic security grounded in the fundamental laws of physics. Nevertheless, practical imperfections can introduce side channels that expose QKD systems to quantum hacking, especially passive attacks that are inherently difficult to detect. In this study, we experimentally and theoretically investigate the upper limit of the backflash attack-a representative passive side-channel threat. Using a fully equipped fiber-based QKD receiver, we demonstrate the feasibility of the attack and reveal its limited capability in distinguishing quantum states. We further develop a theoretical framework to quantify the maximum distinguishability achievable by an eavesdropper, taking into account the broadband spectral nature of backflash photons. The analysis shows that Eve can extract effective key information from at most 95.7% of the backflash photons. Based on these findings, we evaluate the secure key rate of a decoy-state BB84 QKD system under backflash attack. Our results provide a quantitative assessment of the vulnerability of QKD systems to backflash emissions and offer a general methodology to evaluate the practical security of QKD systems.

quant-ph↗

arXiv2025

Spectral side channels of wavelength-division multiplexer in quantum key distribution under laser damage

In the transmitter of a quantum key distribution (QKD) system, a wavelength-division multiplexer (WDM) is typically used to combine quantum and synchronization signals and is directly connected to the quantum channel. As a result, it becomes the first optical component exposed to laser-injection attacks. Therefore, understanding the behavior of WDMs under such attacks is essential for assessing the practical security of QKD systems. In this work, we systematically investigate the characteristics of WDMs under high-power laser illumination. Our experimental results show that certain WDM samples exhibit pronounced changes in their spectral features once the injected laser power surpasses a specific threshold. Taking the Trojan-horse attack as an illustrative example, we further perform a theoretical analysis of the resulting spectral side channel and show that it can reduce the maximum secure transmission distance to below $66.9\%$ of its original value. By combining experimental observations with theoretical modeling, this study advances the understanding of the influence of WDMs on the practical security of QKD systems.

quant-ph↗

arXiv2025

Bayesian Distributional Models of Executive Functioning

This study uses controlled simulations with known ground-truth parameters to evaluate how Distributional Latent Variable Models (DLVM) and Bayesian Distributional Active LEarning (DALE) perform in comparison to conventional Independent Maximum Likelihood Estimation (IMLE). DLVM integrates observations across multiple executive function tasks and individuals, allowing parameter estimation even under sparse or incomplete data conditions. To establish known-ground truth, we uniformly sample individual sessions from a neural network learned latent space and map them to distributional cognitive performance across different tasks. The individual test-items are then sampled from these distributions using either DALE, random procedure or a standard fixed battery approach. When given the same set of observations, DLVM consistently outperformed IMLE, especially under smaller amounts of data, and converges faster to highly accurate estimates of the true distributions. In a second set of analyses, DALE adaptively guided sampling to maximize information gain, outperforming random sampling and fixed test batteries, particularly within the first 80 trials. These findings establish the advantages of combining DLVM's cross-task inference with DALE's optimal adaptive sampling, providing a principled basis for more efficient cognitive assessments.

cs.LG↗

arXiv2019

DBRec: Dual-Bridging Recommendation via Discovering Latent Groups

In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridging recommendation model (DBRec). DBRec performs latent user/item group discovery simultaneously with collaborative filtering, and interacts group information with users/items for bridging similar users/items. Therefore, a user's preference over an unobserved item, in DBRec, can be bridged by the users within the same group who have rated the item, or the user-rated items that share the same group with the unobserved item. In addition, we propose to jointly learn user-user group (item-item group) hierarchies, so that we can effectively discover latent groups and learn compact user/item representations. We jointly integrate collaborative filtering, latent group discovering and hierarchical modelling into a unified framework, so that all the model parameters can be learned toward the optimization of the objective function. We validate the effectiveness of the proposed model with two real datasets, and demonstrate its advantage over the state-of-the-art recommendation models with extensive experiments.

cs.IR↗