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Proceedings of the 33rd USENIX Conference on security Symposium

作者： Jiaxu Zhao Yuekang Li Yanyan Zou Zhaohui Liang Yang Xiao Yeting Li Bingwei Peng Nanyu Zhong Xinyi Wang Wei Wang Wei Huo Institute of Information Engineering Chinese Academy of Sciences China and School of Cyber Security University of Chinese Academy of Sciences China and Key Laboratory of Network Assessment Technology Chinese Academy of Sciences China and Beijing Key Laboratory of Network Security and Protection Technology China University of New South Wales Australia Institute of Information Engineering Chinese Academy of Sciences China and Key Laboratory of Network Assessment Technology Chinese Academy of Sciences China and Beijing Key Laboratory of Network Security and Protection Technology China

ISBN: (纸本)9781939133441

IoT devices have significantly impacted our daily lives, and detecting vulnerabilities in embedded systems early on is critical for ensuring their security. Among the existing vulnerability detection techniques for embedded systems, static taint analysis has been proven effective in detecting severe vulnerabilities, such as command injection vulnerabilities, which can cause remote code execution. Nevertheless, static taint analysis is faced with the problem of identifying sources comprehensively and *** paper presents LARA, a novel static taint analysis technique to detect vulnerabilities in embedded systems. The design of LARA is inspired by an observation that pertains to semantic relations within and between the code and data of embedded software: user input entries can be categorized as URIs or keys (data), and identifying their handling code (code) and relations can help systematically and comprehensively identify the sources for taint analysis. Transforming the observation into a practical methodology poses challenges. To address these challenges, LARA employs a combination of pattern-based static analysis and large language model(LLM)-aided analysis, aiming to replicate how human experts would utilize the findings during analysis and enhance it. The pattern-based static analysis simulates human experience, while the LLM-aided analysis captures the way human experts perceive code semantics. We implemented LARA and evaluated it on 203 IoT devices from 21 vendors. In general, LARA detects 556 and 602 more known vulnerabilities than SATC and KARONTE while reducing false positives by 57.0% and 54.3%. Meanwhile, with more sources and sinks from LARA, EMTAINT can detect 245 more vulnerabilities. To date, LARA has found 245 0-day vulnerabilities in 57 devices, all of which were confirmed or fixed with 162 CVE IDs assigned.

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On the -differential spectra of two classes of power functions over finite fields

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Applicable Algebra in Engineering, Communication and Computing 2025年 1-30页

作者： Liu, Qian Huang, Zhiwei Chen, Zhixiong Jiang, Rong Zhang, Liupiao Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Fujian Key Laboratory of Financial Information Processing Putian University Putian China School of Computer Science and Technology Dongguan University of Technology Dongguan China

Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. [7]. As an extension of the differential uniformity, it is theoretically appealing to determine the properties of c-differential uniformity and the corresponding c-differential spectrum. In this paper, based on certain quadratic character sums and two special elliptic curves over $$\mathbb {F}_p$$ , the $$(-1)$$ -differential spectra of the following two classes of power functions over $$\mathbb {F}_{p^n}$$ is completely determined: (1) $$f_1(x)=x^{\frac{p^n+3}{2}}$$ , where $$p>3$$ and $$p\equiv 3\pmod 4$$ ; (2) $$f_2(x)=x^{p^n-3}$$ , where $$p>3$$ . The obtained result shows that the $$(-1)$$ -differential spectra of $$f_1(x)$$ and $$f_2(x)$$ can be expressed explicitly in terms of n. Moreover, an upper bound of the c-differential uniformity of $$f_2(x)$$ is given.

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THC-DL: Three-Party Homomorphic Computing Delegation Learning for Secure Optimization

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IEEE Internet of Things Journal 2025年

作者： Yang, Ze Tian, Youliang Xiong, Jinbo Niu, Kun Li, Mengqian Zhang, Xinyu Ma, Jianfeng Guizhou University Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology College of Computer Science and Technology Guiyang550025 China Guizhou University College of Big Data and Information Engineering Guiyang550025 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Xidian University State Key Laboratory of Integrated Services Network Xi’an710071 China Xidian University State Key Laboratory of Integrated Services Network School of Cyber Engineering Xi’an710071 China

Delegation Learning(DL) flourishes data sharing, enabling agents to delegate data to the cloud for model training. To preserve privacy, homomorphic encryption (HE) offers an effective solution for privacy-preserving machine learning (PPML) in delegation learning, yet faces critical challenges in functionality (non-linear activation support), practicality (ciphertext blow-up from iterative computations), and security (data leakage risks caused by public knowledge of the mathematical principles applied in model training). To tackle these challenges, we propose THC-DL, a three-party HE framework addressing these challenges holistically for the first time. We elaborately design the ciphertext secure comparison (DL-CSC) protocol to satisfy secure comparison with private inputs, enabling efficient non-linear operations with O(1) communication complexity that reduces runtime to 12.5% of the DGK (Dolev-Greensmith-Kent protocol, the well-known comparison protocol). Second, we construct a Truncation-Mapping (Tru-Map) scheme, a mechanism that transforms input data by truncating and mapping it into a domain that facilitates more efficient processing, and the addition of truncated mapped data to resolve ciphertext blow-up by adaptively scaling ciphertexts during iterative training, ensuring correctness. Third, we formalize data leakage risks in HE-based quadratic convex optimization (standard in ML) and apply THC-DL to construct a secure optimization scheme. Theoretical analysis confirms THC-DL’s resilience against input recovery attacks, even when adversaries exploit public model parameters. Experiments on a real-world platform validate DL-CSC’s efficiency and scalability while reducing the computational complexity and communication complexity from O(n) to O(1) where n denotes the length of the input bits. © 2014 IEEE.

关键词： Convex optimization

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ILP-Based Heuristics for the Multi-Modal Stable Matching Problem

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Tsinghua Science and Technology 2025年第2期30卷 479-487页

作者： Yang Yang Rolf H.Möhring Junteng Song Yicheng Xu Yong Zhang Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 101408China School of Applied Mathematics and Computer Science Technische Universität BerlinBerlin 10623Germany School of Mathematics and Information Science Hebei UniversityBaoding 071002China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin 541004China

In this paper,we investigate the stable matching problem with multiple preferences in bipartite graphs,where each agent has various preference lists for all available partners with respect to different *** problem requires that each matched agent must have exactly one partner and the obtained matching should be stable for all *** our main contribution,we present an integer linear programming(ILP)model for determining whether there exists a globally stable matching in bipartite graphs,which has been proved to be *** the time consumed for solving ILPs might dramatically increase as the size of instances grows,we develop a preprocessing technique that helps to eliminate pairs that will never be a member of any globally stable matching and thus accelerates the computing *** perform experiments on randomly generated preference lists and observe a significant speedup when we preprocess the instance before solving the *** there does not need to exist a perfect matching that is stable for all given criteria,we extend our ILP to the optimized version of the aforementioned problem,which asks to find a matching with maximum cardinality that is stable among all matched agents.

关键词： stable matching multi-modal integer linear programming(ILP)

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Fratricide! Hijacking in Android Multi-Window

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 3075-3092页

作者： Guo, Chenkai Wang, Tianhong Wang, Qianlu Dong, Naipeng Luo, Xiangyang Liu, Zheli Nankai University College of Cyber Science Tianjin300350 China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation Anhui 230007 China Haihe Lab of ITAI Tianjin300350 China Nankai University College of Computer Science Tianjin300350 China University of Queensland School of Information Technology and Electrical Engineering Saint LuciaQLD4072 Australia State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

Android’s multi-window solutions allow several apps to coexist on one screen, providing powerful functionalities and appealing visuals for modern mobile devices. However, this opens the door for potential malware hijacking attacks. In our study, we unveiled Android’s insufficient security measures against malware activity in multi-window modes, and identified strategies on how to bypass these measures. We first introduced long-term monitoring methods through background services or malicious sub-window activities, and then designed and implemented 7 multi-window hijacking strategies to achieve multi-window hijacking in the three mainstream multi-window modes. To evaluate the effectiveness and impact of the attacks, we ran the hijacking in different system versions supporting multi-window solutions, and simulated the hijacking on 262 popular apps. All of the apps suffer from at least two types of multi-window hijacking attacks, and more than 65% of them can be attacked by all the 7 multi-window hijacking attacks. Furthermore, we designed and distributed a questionnaire to gather user opinions towards these attacks. 85% of respondents believe that the multi-window hijacking is highly covert and difficult to defend against. Of significance, we received acknowledgement and award fund from development teams of impactful apps. © 2004-2012 IEEE.

关键词： Android malware

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CL-TAP: An Efficient Certificateless Based Trusted Access Protocol for WLAN

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Chinese Journal of Electronics 2025年第1期23卷 142-146页

作者： Zhuo Ma Fenghua Li Jianfeng Ma Wenjiang Ji School of Computer Science and Technology Xidian University Xi'an Shannxi China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China

Based on the certificateless public key cryptography and the trusted computing technologies, a certificateless based trusted access protocol for WLAN (Wireless local area networks) is proposed. Such protocol realizes the mutual authentication and unicast session key agreement between STA and AP within 3 protocol rounds. In particular, the platform authentication and integrity verification are achieved during the authentication procedure. The security properties of the new protocol are examined using the Extended Canetti-Krawczyk security model. The analytic comparisons show that the new protocol is very efficient in both computing and communications.

关键词： Wireless LAN Trusted computing Unicast Computational modeling Authentication Access protocols Public key cryptography Delays security Research and development

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Data Dynamics Enabled Privacy-Preserving Public Batch Auditing in Cloud Storage

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Chinese Journal of Electronics 2025年第2期23卷 297-301页

作者： Zhengwei Ren Lina Wang Qianhong Wu Ruyi Deng School of Computer Wuhan University Wuhan China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan China

Many schemes have been present to tackle data integrity and retrievability in cloud storage. Few of existing schemes support data dynamics, public verification and protect data privacy simultaneously. We propose a public auditing scheme which enables privacy-preserving, data dynamics and batch auditing. A data updating information table is designed to record the status information of the data blocks and facilitate data dynamics. Homomorphic authenticator and random masking technologies are exploited to protect data privacy for data owners. The scheme employs a Trusted third party auditor (TTPA) to verify the data integrity without learning any information about the data content during the auditing process. The scheme also allows batch auditing so that TTPA can process multiple auditing requests simultaneously which greatly accelerates the auditing process. security and performance analysis show that our scheme is secure and feasible.

关键词： Cloud computing Data privacy Data integrity Medical services Performance analysis security Proposals Protection

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MCFTNet: Multimodal Cross-layer Fusion Transformer network for Hyperspectral and LiDAR Data Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12803-12818页

作者： Huang, Wei Wu, Tianren Zhang, Xueyu Li, Liangliang Lv, Ming Jia, Zhenhong Zhao, Xiaobin Ma, Hongbing Vivone, Gemine Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi830017 China Guangxi University School of Computer and Electronic Information Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Tsinghua University Department of Electronic Engineering Beijing100084 China National Research Council Tito85050 Italy NBFC National Biodiversity Future Center Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral image (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transformer models use an external classification (CLS) token that is randomly initialized and often struggles to generalize effectively. In this paper, we combine the strengths of HSI and light detection and ranging (LiDAR) data, using LiDAR as an external CLS token, which significantly enhances classification accuracy and reliability. We propose a new multimodal cross-layer fusion transformer network (MCFTNet), integrating CNNs with the latest Transformer networks. It includes a CNN for extracting spatial features and a hybrid cross-patch attention mechanism (HyCrossAttention) for land cover classification, leveraging LiDAR data to generate CLS and HSI patch tokens. More importantly, to reduce the loss of valuable information during the layer-by-layer propagation, we designed cross-layer skip connections. Through adaptive learning, these cross-layer fusions help address the gradient vanishing problem in deep networks while preserving early-layer features. This enables the model to better integrate information from different layers, enhancing both its stability and performance. We carried out in - depth experiments on commonly - used benchmark datasets, specifically, the University of Houston dataset, the Trento dataset, and the University of Southern Mississippi Gulf Park (MUUFL) dataset. We compared the results of the proposed MCFTNet model with those obtained from state-of-the-art Transformer models, classical CNNs, and traditional classifiers. As a result, the MCFTNet model outshone them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Oracles-Based Framework for Introducing Governance Entitlements Across DAOs 30th

Oracles-Based Framework for Introducing Governance Entitleme...

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30th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2024

作者： Xu, Yuting Chen, Lin Yu, Xiang Liu, Kui Guangdong Key Laboratory of Blockchain Security Guangzhou University Guangzhou University Guangzhou510006 China School of Electronics and Information Engineering Taizhou University Taizhou318000 China Guangzhou University Technology Center of Network and Modern Education Technology Guangzhou Guangdong510006 China

ISBN: (纸本)9783031829062

With the arose of digital era, Decentralized Autonomous Organization (DAO), as the primary representatives of blockchain decentralized application organizational forms, have consistently showcased their autonomous governances advantages of transparency and trustlessness. Governance is the core of DAOs, directly determines the development of the entire organization. As current DAO governance is limited by inefficiency, the mainstream approach in the industry is to improve governance efficiency by optimizing governance mechanisms and designing incentive mechanisms to increase member participation. However, these approaches do not solve the problem of sharing governance entitlements across different organizations in a scalable way. This limitation results in inefficiencies in governance. Therefore, this paper proposes a framework which introduces governance entitlements based on oracles for a DAO platform. The platform uses oracles to verify the user’s external identity and anchor it with the internal platform identity. It also collects external governance data to achieve cross-organizational governance entitlements authentication, thus improving governance efficiency. Furthermore, we perform a formal analysis of the cross-chain authentication mechanism to demonstrate its security. Due to the differences in the governance entitlements of different DAOs, it lacks fairness to directly share the external governance entitlements of users. Therefore, this paper proposes a model for calculating governance equity that converts external equity classification into internal governance entitlements to ensure fairness in governance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Efficiency

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Persuasion Should be Double-Blind: A Multi-Domain Dialogue Dataset With Faithfulness Based on Causal Theory of Mind

arXiv

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arXiv 2025年

作者： Zhang, Dingyi Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Persuasive dialogue plays a pivotal role in human communication, influencing various domains. Recent persuasive dialogue datasets often fail to align with real-world interpersonal interactions, leading to unfaithful representations. For instance, unrealistic scenarios may arise, such as when the persuadee explicitly instructs the persuader on which persuasion strategies to employ, with each of the persuadee’s questions corresponding to a specific strategy for the persuader to follow. This issue can be attributed to a violation of the "Double Blind" condition, where critical information is fully shared between participants. In actual human interactions, however, key information—such as the mental state of the persuadee and the persuasion strategies of the persuader—is not directly accessible. The persuader must infer the persuadee’s mental state using Theory of Mind capabilities and construct arguments that align with the persuadee’s motivations. To address this gap, we introduce ToMMA, a novel multi-agent framework for dialogue generation that is guided by causal Theory of Mind. This framework ensures that information remains undisclosed between agents, preserving "double-blind" conditions, while causal ToM directs the persuader’s reasoning, enhancing alignment with human-like persuasion dynamics. Consequently, we present CToMPersu, a multi-domain, multi-turn persuasive dialogue dataset that tackles both double-blind and logical coherence issues, demonstrating superior performance across multiple metrics and achieving better alignment with real human dialogues. Our dataset and prompts are available at https://***/DingyiZhang/ToMMA-CToMPersu. Copyright © 2025, The Authors. All rights reserved.

关键词： Economic and social effects

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