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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Yuying Liu, Gaoyang Wang, Chen Yang, Yang Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Hubei University Wuhan China Hubei Key Laboratory of Smart Internet Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350368741

Retrieval-Augmented Generation (RAG) is a state-of-the-art technique that mitigates issues such as hallucinations and knowledge staleness in Large Language Models (LLMs) by retrieving relevant knowledge from an external database to assist in content generation. Existing research has demonstrated potential privacy risks associated with the LLMs of RAG. However, the privacy risks posed by the integration of an external database, which often contains sensitive data such as medical records or personal identities, have remained largely unexplored. In this paper, we aim to bridge this gap by focusing on membership privacy of RAG's external database, with the aim of determining whether a given sample is part of the RAG's database. Our basic idea is that if a sample is in the external database, it will exhibit a high degree of semantic similarity to the text generated by the RAG system. We present S2MIA, a Membership Inference Attack that utilizes the Semantic Similarity between a given sample and the content generated by the RAG system. With our proposed S2MIA, we demonstrate the potential to breach the membership privacy of the RAG database. Extensive experimental results demonstrate that S2MIA outperforms five existing MIAs, even when the system is protected by three representative defenses. © 2025 IEEE.

关键词： Large Language Models Membership Inference Attacks Retrieval-Augmented Generation

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Enhancing graph representation learning via type-aware decoupling and node influence allocation

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Applied Intelligence 2025年第6期55卷 1-14页

作者： Zhu, Guochang Hu, Jun Liu, Li Zhang, Qinghua Wang, Guoyin School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongwen Road Chongqing Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongwen Road Chongqing Chongqing400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing400065 China

The traditional graph representation methods can fit the information of graph with low-dimensional vectors, but they cannot interpret their composition, resulting in insufficient security. Graph decoupling, as a method of graph representation, can analyze the latent factors composing the graph representation vectors. However, in current graph decoupling methods, the number of factors is a hyperparameter, and enforce uniform decoupling vector dimensions which leads to information loss or redundancy. To address these issues, we propose a type-aware graph decoupling based on influence called Variational Graph Decoupling Auto-Encoder (VGDAE). It uses node labels as interpretable and objectively existing natural semantics for decoupling and allocates embedding space based on node influence, addressing the issues of manually setting the number of factors in traditional graph decoupling and the mismatch between node information size and embedding space. On the Cora, Citeseer, and fb-CMU datasets, VGDAE shows the impact of different node classes as decoupling targets on classification tasks. Furthermore, we perform visualization of the representations, VGDAE exhibits performance improvements of 2% in classification tasks and 12% in clustering tasks when compared with baseline models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Graph embeddings

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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge Computing

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Ming Northeastern University School of Computer Science and Engineering Engineering Research Center of Security Technology of Complex Network System Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system information. We first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multi-per-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Differential-Linear Attacks on Ballet Block Cipher

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密码学报(中英文) 2025年第2期12卷 469-488页

作者： ZHOU Yu CHEN Si-Wei XU Sheng-Yuan XIANG Ze-Jun ZENG Xiang-Yong Key Laboratory of Intelligent Sensing System and Security Ministry of EducationSchool of Cyber Security and TechnologyHubei UniversityWuhan 430062China Department of Fundamental Courses Shandong University of Science and TechnologyTaian 271000China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and StatisticsHubei UniversityWuhan 430062China

Ballet is one of the finalists of the block cipher project in the 2019 National Cryptographic Algorithm Design *** study aims to conduct a comprehensive security evaluation of Ballet from the perspective of differential-linear(DL)***,we present an automated search for the DL distinguishers of Ballet based on MILP/*** the versions with block sizes of 128 and 256 bits,we obtain 16 and 22 rounds distinguishers with estimated correlations of 2^(-59.89)and 2^(-116.80),both of which are the publicly longest *** addition,this study incorporates the complexity information of key-recovery attacks into the automated model,to search for the optimal key-recovery attack structures based on DL *** a result,we mount the key-recovery attacks on 16-round Ballet-128/128,17-round Ballet-128/256,and 21-round Ballet-256/*** data/time complexities for these attacks are 2^(108.36)/2^(120.36),2^(115.90)/2^(192),and 2^(227.62)/2^(240.67),respectively.

关键词： Ballet block cipher differential-linear(DL)cryptanalysis MILP/MIQCP distinguisher key-recovery attacks

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Improved quantum image weighted average filtering algorithm

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Quantum Information Processing 2025年第5期24卷 1-24页

作者： Yuan, Suzhen Li, Xianli Yinxia, Shu Qing, Xianrong Deng, Jermiah D. College of Opto Electronic Engineering Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China School of Computing University of Otago Dunedin Dunedin9054 New Zealand School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China

Average filtering plays a vital role in image smoothing tasks. However, existing quantum image weighted average filtering methods suffer from high circuit complexity. Therefore, this paper proposes an improved quantum color image weighted average filtering algorithm and its corresponding quantum circuit. First, we improve the quantum circuit to prepare classical color images into a quantum state. Then, an improved quantum divider is developed, and a weighted average filter is constructed using basic quantum image processing modules. Next, to enhance the universality of the filter, a quantum comparator with lower circuit complexity is used to design a noise detection module for distinguishing noise from real signals. Finally, a quantum circuit for color image weighted average filtering is designed, and simulations are conducted on the IBM Quantum Experience (IBM Q) platform to verify the feasibility of our algorithm. The analysis shows that compared with existing methods, this method significantly reduces the circuit complexity and has better filtering performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Image enhancement

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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi’an710049 China Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China Peking University School of Computer Science Beijing100871 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University Xi'an710049 China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports 3.12x more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： Network topology

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Federated Hybrid-Supervised Learning for Universal Medical Image Segmentation

Federated Hybrid-Supervised Learning for Universal Medical I...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zheng, Shenhai Wen, Sian Li, Congyu Chen, Qing Li, Laquan College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China School of Science Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350368741

Federated Learning (FL) is an advanced technology that tackles the challenge of blocked data arising from privacy concerns, enabling the training of deep learning models without the need for data sharing. However, FL faces difficulties with heterogeneous data and limited annotations in medical image segmentation. Motivated by this discovery, this study proposes a novel hybrid-supervised federated learning method (FedSLAG) that explores various types of annotations in medical imaging. To focus more on weakly-supervised and unsupervised scenarios within hybrid-supervised learning, a federated Gaussian enhancement module was proposed for heterogeneous sparse annotations (points and scribbles). The feature extraction module combines the features of multiple weakly-supervised clients and establishes the correlation of similar pixels, thus making up for the deficiency of scarce annotations and the insufficient feature extraction capability of a single machine. Then, a two-stage broadcast mechanism based on supervision sparsity was proposed to alleviate optimization deviation in local models. Experiments on breast tumor and skin lesion segmentation tasks demonstrate significant efficiency gains as well as highly competitive segmentation accuracy in many hybrid-supervised situations. Our codes are available at: https://***/TrivenDev/FedSLAG. © 2025 IEEE.

关键词： federated learning hybrid-supervised learning medical image segmentation

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Five-dimensional Floquet topological semimetals with emergent Yang monopoles and linked Weyl surfaces

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Physical Review B 2025年第4期111卷 045302-045302页

作者： Zheng-Rong Liu Rui Chen Bin Zhou Department of Physics Hubei University Wuhan 430062 China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei University Wuhan 430062 China

Recently, Floquet topological matter has attracted significant attention for its potential to reveal novel topological phases inaccessible in static systems. In this paper, we investigate the effect of a time-periodic driving on the five-dimensional (5D) normal insulators. We show that the time-periodic driving can induce a topological phase transition from a TP (time reversal combined with space inversion) symmetry-preserving normal insulator to a 5D Floquet topological semimetal with emergent Yang monopoles characterized by the second Chern number. Additionally, we show that this time-periodic driving can also lead to a topological phase transition from a TP symmetry-breaking normal insulator to a 5D Floquet topological semimetal with a Hopf link formed by Weyl surfaces. Besides, further increasing the strength of the time-periodic driving, the two 5D Floquet topological semimetal phases are transformed into the 5D Floquet Chern insulators. Our paper is helpful for future studies in higher-dimensional Floquet systems.

关键词： Topology

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POINTERGUESS: targeted password guessing model using pointer mechanism 24

POINTERGUESS: targeted password guessing model using pointer...

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Proceedings of the 33rd USENIX Conference on security Symposium

作者： Kedong Xiu Ding Wang College of Cyber Science Nankai University Tianjin China and Key Laboratory of Data and Intelligent System Security (NKU) Ministry of Education Tianjin China and Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin China

ISBN: (纸本)9781939133441

Most existing targeted password guessing models view users' reuse behaviors as sequences of edit operations (e.g., insert and delete) performed on old passwords. These atomic edit operations are limited to modifying one character at a time and cannot fully cover users' complex password modification behaviors (e.g., modifying the password structure). This partially leads to a significant gap between the proportion of users' reused passwords and the success rates that existing targeted password models can achieve. To fill this gap, this paper models users' reuse behaviors by focusing on two key components: (1) What they want to copy/keep; (2) What they want to tweak. More specifically, we introduce the pointer mechanism and propose a new targeted guessing model, namely POINTERGUESS. By hierarchically redefining password reuse from both personal and population-wide perspectives, we can accurately and comprehensively characterize users' password reuse behaviors. Moreover, we propose MS-POINTERGUESS, which can employ the victim's multiple leaked *** employing 13 large-scale real-world password datasets, we demonstrate that POINTERGUESS is effective: (1) When the victim's password at site A (namely pwA) is known, within 100 guesses, the average success rate of POINTERGUESS in guessing her password at site B (namely pwB, pwA ≠ pwB) is 25.21% (for common users) and 12.34% (for security-savvy users), respectively, which is 21.23%~71.54% (38.37% on average) higher than its foremost counterparts; (2) When not excluding identical password pairs (i.e., pwA can equal pwB), within 100 guesses, the average success rate of POINTERGUESS is 48.30% (for common users) and 28.42% (for security-savvy users), respectively, which is 6.31%~15.92% higher than its foremost counterparts; (3) Within 100 guesses, the MS-POINTERGUESS further improves the cracking success rate by 31.21% compared to POINTERGUESS.

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DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed Problems in Malicious Traffic Detection

DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed P...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guoliang Li Ruiqi Zhang Zhe Sun Lingkai Xing Yu Zhang College of Cyber Science Nankai University China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education China College of Computer Science Nankai University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, deep learning based malicious traffic detection (MTD) systems have demonstrated remarkable success. However, their effectiveness tend to decrease because most malicious traffic datasets are suffered from noisy-labeled and long-tailed problems. While numerous approaches have been developed to address these two problems individually, they become inefficient when confronting the combined challenge of noisy long-tailed data, as they typically tackle only a single adverse factor at a time. This paper proposes a two-stage method called Distribution-aware sample Selection and Dynamic Instance-based Relabeling (DSDIR), which simultaneously addresses the impacts of noisy-labeled and long-tailed problems. In the first stage, a noise-independent clean sample selection method is designed to obtain a clean dataset, which converts negative effects of the long-tailed problem into positive ones. In the second stage, dynamic instance-based relabeling is designed to train a model and improve the dataset’s quality simultaneously. Eventually, DSDIR not only produces a balanced and noise-tolerant model but also obtains a clean dataset. Experimental results demonstrate that in the high noise condition of 60% and 80%, the accuracy rate of DSDIR is 5% higher than the state-of-the-art methods. Our code is available at https://***/nku-ligl/DSDIR.

关键词： Deep learning Computer vision Heavily-tailed distribution Codes Accuracy Noise Speech recognition Acoustics Noise measurement Speech processing

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