arXiv subjects

Yufei Zhou

Publications and source records attributed to Yufei Zhou.

16 recordsLinked to original sources

arXiv2026

Ciphertext-Native Watermarking for RLWE-Based Homomorphic Encryption

Homomorphic encryption (HE) schemes based on the Ring Learning with Errors (RLWE) problem have been rapidly developed and widely applied to secure computation tasks, such as privacy-preserving deep learning inference and database queries. However, existing HE schemes mainly focus on the feasibility and efficiency of homomorphic computation, while practical requirements including ciphertext copyright protection, provenance tracking, and computation supervision remain largely unexplored. In this work, we propose a watermarking technique for RLWE-based HE ciphertexts. By exploiting the algebraic structure of RLWE polynomials, we embed watermark information into ciphertext noise without affecting plaintext correctness. To address watermark degradation caused by homomorphic operations, we introduce two practical schemes. The first scheme, ARWMark, leverages noise stratification to achieve robustness against homomorphic additive operations. The second scheme, MRWMark, is based on the roots of a linear equation and supports zero-bit watermarking while remaining robust against both homomorphic addition and multiplication. We provide rigorous theoretical analysis demonstrating that the proposed schemes preserve the original security of HE while maintaining correctness and watermark robustness. Extensive experiments further validate the effectiveness and practicality of the proposed watermarking schemes.

cs.CR↗

arXiv2026

NFSA: Non-Forward Secure Aggregation with One Server via Two Layer Secret Sharing

Federated Learning (FL) enables collaborative model training while preserving privacy by keeping data local. However, the risk of sensitive data leakage through model updates necessitates the use of secure aggregation protocols. Existing server-based secure aggregation protocols typically require the server to forward sensitive data shared between users, which increases communication overhead and introduces potential security risks. In this work, we propose a novel secure aggregation protocol based on two-layer secret sharing to address these issues. By combining Shamir's Secret Sharing with 2-out-of-2 additive secret sharing using a Pseudo-Random Function (PRF), our protocol eliminates direct communication between users, thereby removing the need for the server to forward data. We further extend the protocol with Key-homomorphic PRF (KhPRF) to support high-dimensional data aggregation and apply it to FL, enabling one-shot secure aggregation with a single server and no intermediary data forwarding. To reduce user overhead, we design a new encoding method based on the Chinese Remainder Theorem for the almost KhPRF-based mask, reducing the number of KhPRF calls and mitigating the model update expansion issue after masking. Experimental results show that our scheme significantly outperforms existing methods in terms of auxiliary node overhead. For instance, when the number of users is 100, our scheme improves communication efficiency by nearly 100 times and reduces computational overhead by approximately 17\%. Moreover, user computation time can be reduced by 51\% to 75\% when the input length is $2^{18}$.

cs.CR↗

arXiv2026

HKJudge: A Legal Discourse-Annotated Corpus for Interpreting What Courts Find, How They Reason, and What They Rule

Court judgments are central to legal practice and jurisprudence, yet discourse analysis of Hong Kong judgments has received limited attention, owing largely to the absence of expert-annotated corpora. We introduce the Hong Kong Judgment Discourse Dataset (HKJudge), the first sentence-level expert-annotated legal discourse corpus. HKJudge includes criminal judgments across all five levels of HK's court hierarchy, comprising $\sim$290k sentences and $\sim$6.5 million tokens, fully annotated by legal linguistics experts. We design a two-tier discourse schema that captures what facts a court finds, how it reasons, and what it rules. At the sentence level, each sentence is assigned one of 26 rhetorical roles. At the span level, sentences are further annotated with three sentencing elements (charge, imprisonment term, fine). Ten legal linguistics annotators produced the annotations with an inter-annotator agreement of $\kappa = 0.8$. We formulate two tasks on HKJudge, termed rhetorical role classification and legal element extraction, and provide the first benchmark evaluation of four BERT-based models, two open-source LLMs under zero-shot and fine-tuning settings, and four commercial LLMs on both tasks. Our work demonstrates the value of sentence-level discourse annotation for modeling the structure of HK judgments and provides a rich data foundation for future work on legal judgment prediction. The HKJudge dataset and code are available at https://github.com/xuanxixi/HKJudge.

cs.CL↗

arXiv2026

Privacy-Preserving High-Resolution Image Gradient Computation Based on Fully Homomorphic Encryption

With growing emphasis on privacy protection, homomorphic encryption (HE) has emerged as a core method for privacy-preserving image processing, as it enables operations directly on encrypted data. However, existing research predominantly focuses on low-resolution image processing, and techniques for privacy-preserving high-resolution image processing remain underexplored. As the image size increases, the HE parameters must be adjusted accordingly, and directly applying existing methods can lead to significant computational overhead. In this work, we propose a multi-ciphertext privacy-preserving framework for large images, enabling efficient image encryption and computation under the semi-honest model. Specifically, we divide the large image into multiple sub-images, which allows us to maintain smaller HE parameters and reduce key size. By parallel processing the sub-image ciphertexts and introducing a new bootstrapping placement strategy, we significantly reduce encryption overhead and enhance user experience. On the server side, we optimize the large image convolution operation through a repeated packing technique and implement the Sobel operator computation based on HE. To improve gradient direction calculation for the Sobel operator, we introduce a new polynomial approximation method for the reciprocal function based on the sign function, which can be applied to other HE-based protocols.

cs.CR↗

arXiv2026

Verifiable Secure Aggregation via Dual Servers with Linear Tags in Federated Learning

Federated learning (FL) enables collaborative model training by aggregating local updates without requiring raw data sharing. However, prior studies have shown that servers can exploit gradient inversion to compromise user privacy or manipulate aggregation results, undermining the utility of the global model. To address these concerns, we propose a secure and verifiable aggregation scheme with lightweight cryptographic primitives for FL. Our method leverages pseudo-random functions (PRFs) and a non-colluding dual-server architecture to achieve secure aggregation with mutual server verification, while maintaining communication overhead comparable to plaintext aggregation and a constant verification tag size. Crucially, it preserves user privacy and achieves end-to-end secure aggregation with verification. Moreover, our scheme significantly reduces both user computation and verification overhead, making it suitable for FL with a large number of participants. For instance, with an input dimension of 20K, user computation time is reduced to 18 ms, approximately 7$\times$ faster than OPSA, while verification time decreases to 9.5 ms, approximately 2.4$\times$ faster than OPSA.

cs.CR↗

arXiv2026

Hierarchical Multi-Scale Graph Learning with Knowledge-Guided Attention for Whole-Slide Image Survival Analysis

We propose a Hierarchical Multi-scale Knowledge-aware Graph Network (HMKGN) that models multi-scale interactions and spatially hierarchical relationships within whole-slide images (WSIs) for cancer prognostication. Unlike conventional attention-based MIL, which ignores spatial organization, or graph-based MIL, which relies on static handcrafted graphs, HMKGN enforces a hierarchical structure with spatial locality constraints, wherein local cellular-level dynamic graphs aggregate spatially proximate patches within each region of interest (ROI) and a global slide-level dynamic graph integrates ROI-level features into WSI-level representations. Moreover, multi-scale integration at the ROI level combines coarse contextual features from broader views with fine-grained structural representations from local patch-graph aggregation. We evaluate HMKGN on four TCGA cohorts (KIRC, LGG, PAAD, and STAD; N=513, 487, 138, and 370) for survival prediction. It consistently outperforms existing MIL-based models, yielding improved concordance indices (10.85% better) and statistically significant stratification of patient survival risk (log-rank p < 0.05).

eess.IV↗

arXiv2025

Efficient Mod Approximation and Its Applications to CKKS Ciphertexts

The mod function plays a critical role in numerous data encoding and cryptographic primitives. However, the widely used CKKS homomorphic encryption (HE) scheme supports only arithmetic operations, making it difficult to perform mod computations on encrypted data. Approximating the mod function with polynomials has therefore become an important yet challenging problem. Existing homomorphic mod constructions provide accurate results only within limited subranges of the input domain, leaving the problem of achieving accurate approximation across the entire input domain unresolved.In this work, we propose a novel method based on polynomial interpolation and Chebyshev series to accurately approximate the mod function over all integer points in the bounded input interval. Building upon this, we design two efficient data packing schemes, BitStack and CRTStack, tailored for small-integer inputs in CKKS. These schemes significantly improve the utilization of the CKKS plaintext space and enable efficient ciphertext uploads. Furthermore, we apply the proposed HE mod function to implement a homomorphic rounding operation and a general transformation from additive secret shares to CKKS ciphertexts, achieving accurate ciphertext rounding and complete conversion from secret shares to CKKS ciphertexts. Experimental results demonstrate that our approach achieves high approximation accuracy (up to $10^{-8}$).

cs.CR↗

arXiv2025

Prompt Stability Matters: Evaluating and Optimizing Auto-Generated Prompt in General-Purpose Systems

Automatic prompt generation plays a crucial role in enabling general-purpose multi-agent systems to perform diverse tasks autonomously. Existing methods typically evaluate prompts based on their immediate task performance, overlooking the intrinsic qualities that determine their reliability. This outcome-centric view not only limits interpretability but also fails to account for the inherent stochasticity of large language models (LLMs). In this work, we bring attention to prompt stability-the consistency of model responses across repeated executions-as a key factor for building robust and effective prompt generation systems. To quantify this, we propose semantic stability as a criterion for assessing the response consistency of prompts, and fine-tune a LLaMA-based evaluator to measure it automatically across tasks. These components have enabled us to develop the first stability-aware general-purpose prompt generation system that leverages stability feedback to iteratively enhance both prompt quality and system-level performance. Furthermore, we establish a logical chain between prompt stability and task success by analyzing the structural dependencies within our system, proving stability as a necessary condition for effective system-level execution. Empirical results across general and domain-specific tasks demonstrate that our stability-aware framework improves both accuracy and output consistency. By shifting the focus from one-off results to persistent reliability, our work offers a new perspective on prompt design and contributes practical tools for building more trustworthy general-purpose systems.

cs.AI↗

arXiv2025

Defect-evolved quadrupole higher-order topological nanolasers

Topological photonics have been garnering widespread interest in engineering the flow of light with topological ideas. Strikingly, the recent introduction of higher-order topological insulators has generalized the fundamental framework of topological photonics, endowing counterintuitive strong confinement of light at lower-dimensional boundaries, thus unlocking exciting prospects for the exploration of topological phenomena in fresh routes as well as the design of topology-driven nanoscale light sources. Here, we revealed the photonic quadrupole topological phases can be activated by defect evolution and performed experimental demonstrations of associated nanoscale lasing operation under this paradigm. The quadrupole higher-order topological nanocavity is constructed by two topologically distinct photonic crystal slabs with opposite directions of defect evolution. Stable single mode emission and low lasing threshold in telecom C-band are achieved at room temperature of the defect-evolved quadrupole topological nanolaser. This work reveals new possibilities for photonic quadrupole topological phase transition, providing an intriguing route toward light confinement and modulation under the topological framework.

physics.optics↗

arXiv2024

Multi-Mission Observations of Relativistic Electrons and High-Speed Jets Linked to Shock Generated Transients

Shock-generated transients, such as hot flow anomalies (HFAs), upstream of planetary bow shocks, play a critical role in electron acceleration. Using multi-mission data from NASA's Magnetospheric Multiscale (MMS) and ESA's Cluster missions, we demonstrate the transmission of HFAs through Earth's quasi-parallel bow shock, associated with acceleration of electrons up to relativistic energies. Energetic electrons, initially accelerated upstream, are shown to remain broadly confined within the transmitted transient structures downstream, where betatron acceleration further boosts their energy due to elevated compression levels. Additionally, high-speed jets form at the compressive edges of HFAs, exhibiting a significant increase in dynamic pressure and potentially contributing to driving further localized compression. Our findings emphasize the efficiency of quasi-parallel shocks in driving particle acceleration far beyond the immediate shock transition region, expanding the acceleration region to a larger spatial domain. Finally, this study underscores the importance of multi-scale observational approach in understanding the convoluted processes behind collisionless shock physics and their broader implications.

astro-ph.HE↗

arXiv2024

Efficient Privacy-Preserving KAN Inference Using Homomorphic Encryption

The recently proposed Kolmogorov-Arnold Networks (KANs) offer enhanced interpretability and greater model expressiveness. However, KANs also present challenges related to privacy leakage during inference. Homomorphic encryption (HE) facilitates privacy-preserving inference for deep learning models, enabling resource-limited users to benefit from deep learning services while ensuring data security. Yet, the complex structure of KANs, incorporating nonlinear elements like the SiLU activation function and B-spline functions, renders existing privacy-preserving inference techniques inadequate. To address this issue, we propose an accurate and efficient privacy-preserving inference scheme tailored for KANs. Our approach introduces a task-specific polynomial approximation for the SiLU activation function, dynamically adjusting the approximation range to ensure high accuracy on real-world datasets. Additionally, we develop an efficient method for computing B-spline functions within the HE domain, leveraging techniques such as repeat packing, lazy combination, and comparison functions. We evaluate the effectiveness of our privacy-preserving KAN inference scheme on both symbolic formula evaluation and image classification. The experimental results show that our model achieves accuracy comparable to plaintext KANs across various datasets and outperforms plaintext MLPs. Additionally, on the CIFAR-10 dataset, our inference latency achieves over 7 times speedup compared to the naive method.

cs.LG↗

arXiv2024

DS-ViT: Dual-Stream Vision Transformer for Cross-Task Distillation in Alzheimer's Early Diagnosis

In the field of Alzheimer's disease diagnosis, segmentation and classification tasks are inherently interconnected. Sharing knowledge between models for these tasks can significantly improve training efficiency, particularly when training data is scarce. However, traditional knowledge distillation techniques often struggle to bridge the gap between segmentation and classification due to the distinct nature of tasks and different model architectures. To address this challenge, we propose a dual-stream pipeline that facilitates cross-task and cross-architecture knowledge sharing. Our approach introduces a dual-stream embedding module that unifies feature representations from segmentation and classification models, enabling dimensional integration of these features to guide the classification model. We validated our method on multiple 3D datasets for Alzheimer's disease diagnosis, demonstrating significant improvements in classification performance, especially on small datasets. Furthermore, we extended our pipeline with a residual temporal attention mechanism for early diagnosis, utilizing images taken before the atrophy of patients' brain mass. This advancement shows promise in enabling diagnosis approximately six months earlier in mild and asymptomatic stages, offering critical time for intervention.

eess.IV↗

arXiv2024

DISC: Latent Diffusion Models with Self-Distillation from Separated Conditions for Prostate Cancer Grading

Latent Diffusion Models (LDMs) can generate high-fidelity images from noise, offering a promising approach for augmenting histopathology images for training cancer grading models. While previous works successfully generated high-fidelity histopathology images using LDMs, the generation of image tiles to improve prostate cancer grading has not yet been explored. Additionally, LDMs face challenges in accurately generating admixtures of multiple cancer grades in a tile when conditioned by a tile mask. In this study, we train specific LDMs to generate synthetic tiles that contain multiple Gleason Grades (GGs) by leveraging pixel-wise annotations in input tiles. We introduce a novel framework named Self-Distillation from Separated Conditions (DISC) that generates GG patterns guided by GG masks. Finally, we deploy a training framework for pixel-level and slide-level prostate cancer grading, where synthetic tiles are effectively utilized to improve the cancer grading performance of existing models. As a result, this work surpasses previous works in two domains: 1) our LDMs enhanced with DISC produce more accurate tiles in terms of GG patterns, and 2) our training scheme, incorporating synthetic data, significantly improves the generalization of the baseline model for prostate cancer grading, particularly in challenging cases of rare GG5, demonstrating the potential of generative models to enhance cancer grading when data is limited.

eess.IV↗

arXiv2024

Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Assignments

Automated generation of feedback on programming assignments holds significant benefits for programming education, especially when it comes to advanced assignments. Automated Program Repair techniques, especially Large Language Model based approaches, have gained notable recognition for their potential to fix introductory assignments. However, the programs used for evaluation are relatively simple. It remains unclear how existing approaches perform in repairing programs from higher-level programming courses. To address these limitations, we curate a new advanced student assignment dataset named Defects4DS from a higher-level programming course. Subsequently, we identify the challenges related to fixing bugs in advanced assignments. Based on the analysis, we develop a framework called PaR that is powered by the LLM. PaR works in three phases: Peer Solution Selection, Multi-Source Prompt Generation, and Program Repair. Peer Solution Selection identifies the closely related peer programs based on lexical, semantic, and syntactic criteria. Then Multi-Source Prompt Generation adeptly combines multiple sources of information to create a comprehensive and informative prompt for the last Program Repair stage. The evaluation on Defects4DS and another well-investigated ITSP dataset reveals that PaR achieves a new state-of-the-art performance, demonstrating impressive improvements of 19.94% and 15.2% in repair rate compared to prior state-of-the-art LLM- and symbolic-based approaches, respectively

cs.SE↗

arXiv2023

Human-in-the-loop online just-in-time software defect prediction

Online Just-In-Time Software Defect Prediction (O-JIT-SDP) uses an online model to predict whether a new software change will introduce a bug or not. However, existing studies neglect the interaction of Software Quality Assurance (SQA) staff with the model, which may miss the opportunity to improve the prediction accuracy through the feedback from SQA staff. To tackle this problem, we propose Human-In-The-Loop (HITL) O-JIT-SDP that integrates feedback from SQA staff to enhance the prediction process. Furthermore, we introduce a performance evaluation framework that utilizes a k-fold distributed bootstrap method along with the Wilcoxon signed-rank test. This framework facilitates thorough pairwise comparisons of alternative classification algorithms using a prequential evaluation approach. Our proposal enables continuous statistical testing throughout the prequential process, empowering developers to make real-time decisions based on robust statistical evidence. Through experimentation across 10 GitHub projects, we demonstrate that our evaluation framework enhances the credibility of model evaluation, and the incorporation of HITL feedback elevates the prediction performance of online JIT-SDP models. These advancements hold the potential to significantly enhance the value of O-JIT-SDP for industrial applications.

cs.SE↗

arXiv2023

Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT

The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches. However, the materials science field struggles to effectively utilize the abundance of data, especially in applied disciplines where materials are evaluated based on device performance rather than their properties. This article presents a new natural language processing (NLP) task called structured information inference (SII) to address the complexities of information extraction at the device level in materials science. We accomplished this task by tuning GPT-3 on an existing perovskite solar cell FAIR (Findable, Accessible, Interoperable, Reusable) dataset with 91.8% F1-score and extended the dataset with data published since its release. The produced data is formatted and normalized, enabling its direct utilization as input in subsequent data analysis. This feature empowers materials scientists to develop models by selecting high-quality review articles within their domain. Additionally, we designed experiments to predict the electrical performance of solar cells and design materials or devices with targeted parameters using large language models (LLMs). Our results demonstrate comparable performance to traditional machine learning methods without feature selection, highlighting the potential of LLMs to acquire scientific knowledge and design new materials akin to materials scientists.

cs.CL↗