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A pairing-based signcryption scheme using self-certified public keys

International Journal of Computers and Applications 2007年第3期29卷 278-282页

作者： Li, F. Xin, X. Hu, Y. School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan 610054 China Department of Information and Computing Science Zhengzhou University of Light Industry Zhengzhou Henan 450002 China Key Laboratory of Computer Networks and Information Security Xidian University Xi'an Shaanxi 710071 China

In recent years, many pairing-based signcryption schemes have been proposed. There is, however, an inherent weakness in these schemes: the private key escrow problem, in which the "trusted" private key generator (PKG) can easily impersonate any user at any time without being detected. In this paper we present a pairing-based signcryption scheme using self-certified public keys. In our scheme, users can choose their private keys independently. The public keys of users can be implicitly verified with the subsequent signature verification in a logically single step. A trusted PKG is no longer required. If the dishonest PKG impersonates an honest user to communicate with others, the user can provide a proof of treachery of the PKG afterwards, which is similar to certificate-based systems.

关键词： Public key cryptography

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Robust Disaster Recovery System Model

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Wuhan University Journal of Natural Sciences 2006年第1期11卷 170-174页

作者： WANG Kun SU Rui-dan LI Zeng-xin CAI Zhen ZHOU Li-hua Key Laboratory of Computer Network and Information Security of the Ministry of Education Xidian University Xi＇ an 710071 Shaanxi China National Information Security Engineering and Technology Research Center Beijing 100093 China

Highly security-critical system should possess features of continuous service. We present a new Robust Disaster Recovery System Model （RDRSM）. Through strengthening the ability of safe communications, RDRSM guarantees the secure and reliable command on disaster recovery. Its self-supervision capability can monitor the integrality and security of disaster recovery system itself. By 2D and 3D rea-time visible platform provided by GIS, GPS and RS, the model makes the using, management and maintenance of disaster recovery system easier. RDRSM possesses predominant features of security, robustness and controllability. And it can be applied to highly security-critical environments such as E-government and bank. Conducted by RDRSM, an important E-government disaster recovery system has been constructed successfully. The feasibility of this model is verified by practice. We especially emphasize the significance of some components of the model, such as risk assessment, disaster recovery planning, system supervision and robust communication support.

关键词： continuous service E-government information security disaster recovery high availability risk assessment data backup

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Information-Theoretic Secure Verifiable Secret Sharing over RSA Modulus

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Wuhan University Journal of Natural Sciences 2006年第6期11卷 1849-1852页

作者： QIU Gang WANG Hong WEI Shimin XIAO Guozhen State Key Laboratory of Integrated Sevice Network Xidian University Xi＇an 710071 Shaanxi China State Key Laboratory of Information Security Graduate University of Chinese Academy of Sciences Beijing 100049 China Department of Computer Science and Technology Huaibei Coal Normal College Huaibei 235000 Anhui China

The well-known non-interactive and information-theoretic secure verifiable secret sharing scheme presented by Pedersen is over a large prime. In this paper, we construct a novel non-interactive and information-theoretic verifiable secret sharing over RSA （Rivest, Shamir, Adleman） modulus and give the rigorous security proof. It is shown how to distribute a secret among a group such that any set of k parties get no information about the secret. The presented scheme is generally applied to constructions of secure distributed multiplication and threshold or forward-secure signature protocols.

关键词： verifiable secret sharing information-theoretic secure commitment RSA （Rivest, Shamir, Adleman） modulus

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Disaster recovery system model with e-government case study

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Natural Hazards Review 2006年第4期7卷 145-149页

作者： Wang, Kun Li, Long-Hai Yuan, Feng Zhou, Li-Hua Key Laboratory of Computer Network and Information Security Xidian Univ. Ministry of Education Xi'an 710071 Shaanxi China Dept. of Computer Science and Technology Xidian Univ. Xi'an 710071 Shaanxi China National Information Security Engineering and Technology Research Center Beijing 100093 China

We present a robust disaster recovery system model according to the requests of disaster recovery talent of a key e-government project of China. The model strengthens the safe communication ability and guarantees the secure command on disaster recovery. It introduces a self-supervision power to watch the integrality and security of the disaster recovery system itself. It makes the use, management, and maintenance of the disaster recovery system much easier through the real-time visible platform. This model suits security-critical environments for owning the features of security, robustness, and controllability. It has guided the construction of an e-government disaster recovery system of China. © 2006 ASCE.

关键词： Disaster prevention

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A Systematic Literature Review of Robust Federated Learning: Issues, Solutions, and Future Research Directions

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ACM computing Surveys 1000年

作者： Md Palash Uddin Yong Xiang Mahmudul Hasan Jun Bai Yao Zhao Longxiang Gao School of Information Technology Deakin University Burwood Australia Deakin University Burwood Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Academy of Sciences Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial data and model attacks, noisy updates, and label-flipped data issues remain a critical concern. In this paper, we present a systematic literature review using the PRISMA framework to comprehensively analyze existing research on robust FL. Through a rigorous selection process using six key databases (ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Web of Science, and Scopus), we identify and categorize 244 studies into eight themes of ensuring robustness in FL: objective regularization, optimizer modification, differential privacy employment, additional dataset requirement and decentralization orchestration, manifold, client selection, new aggregation algorithms, and aggregation hyperparameter tuning. We synthesize the findings from these themes, highlighting the various approaches and their potential gaps proposed to enhance the robustness of FL models. Furthermore, we discuss future research directions, focusing on the potential of hybrid approaches, ensemble techniques, and adaptive mechanisms for addressing the challenges associated with robust FL. This review not only provides a comprehensive overview of the state-of-the-art in robust FL but also serves as a roadmap for researchers and practitioners seeking to advance the field and develop more robust and resilient FL systems.

关键词： Federated Learning Robustness Attack Countermeasure Defence.

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IBAQ: Frequency-Domain Backdoor Attack Threatening Autonomous Driving via Quadratic Phase

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Jinghan Qiu Honglong Chen Junjian Li Yudong Gao Junwei Li Xingang Wang College of Control Science and Engineering China University of Petroleum China the Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology China

The rapid evolution of backdoor attacks has emerged as a significant threat to the security of autonomous driving models. An attacker injects a backdoor into the model by adding triggers to the samples, which can be activated to manipulate the model’s inference. Backdoor attacks can lead to severe consequences, such as misidentifying traffic signs during autonomous driving, posing a risk of causing traffic accidents. Recently, there has been a gradual evolution of frequency-domain backdoor attacks. However, since the change of both amplitude and its corresponding phase will significantly affect image appearance, most of the existing frequency-domain backdoor attacks change only the amplitude, which results in a suboptimal efficacy of the attack. In this work, we propose an attack called IBAQ, to solve this problem by blurring semantic information of the trigger image through the quadratic phase. Initially, we convert the trigger and benign sample to YCrCb space. Then, we perform the fast Fourier transform on the Y channel, blending the trigger image’s amplitude and quadratic phase linearly with the benign sample’s amplitude and phase. IBAQ achieves covert injection of trigger information within amplitude and phase, enhancing the attack effect. We validate the effectiveness and stealthiness of IBAQ through comprehensive experiments.

关键词： backdoor attack quadrature phase artificial intelligence security

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S3 Agent: Unlocking the Power of VLLM for Zero-Shot Multi-modal Sarcasm Detection

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ACM Transactions on Multimedia computing, Communications, and Applications 1000年

作者： Peng Wang Yongheng Zhang Hao Fei Qiguang Chen Yukai Wang Jiasheng Si Wenpeng Lu Min Li Libo Qin School of Computer Science and Engineering Central South University China National University of Singapore Singapore Harbin Institute of Technology China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China

Multi-modal sarcasm detection involves determining whether a given multi-modal input conveys sarcastic intent by analyzing the underlying sentiment. Recently, vision large language models have shown remarkable success on various of multi-modal tasks. Inspired by this, we systematically investigate the impact of vision large language models in zero-shot multi-modal sarcasm detection task. Furthermore, to capture different perspectives of sarcastic expressions, we propose a multi-view agent framework, S3 Agent, designed to enhance zero-shot multi-modal sarcasm detection by leveraging three critical perspectives: superficial expression, semantic information, and sentiment expression. Our experiments on the MMSD2.0 dataset, which involves six models and four prompting strategies, demonstrate that our approach achieves state-of-the-art performance. Our method achieves an average improvement of 13.2% in accuracy. Moreover, we evaluate our method on the text-only sarcasm detection task, where it also surpasses baseline approaches.

关键词： Natural language processing Multi-modal sarcasm detection Vision Large language model

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Federated Semantic Learning for Privacy-preserving Cross-domain Recommendation

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ACM Transactions on Information Systems 1000年

作者： Ziang Lu Lei Guo Xu Yu Zhiyong Cheng Xiaohui Han Lei Zhu Shandong Normal University China China University of Petroleum (East China) China Hefei University of Technology China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China Tongji University China

In the evolving landscape of recommender systems, the challenge of effectively conducting privacy-preserving Cross-Domain Recommendation (CDR), especially under strict non-overlapping constraints, has emerged as a key focus. Despite extensive research has made significant progress, several limitations still exist: 1) Previous semantic-based methods fail to deeply exploit rich textual information, since they quantize the text into codes, losing its original rich semantics. 2) The current solution solely relies on the text-modality, while the synergistic effects with the ID-modality are ignored. 3) Existing studies do not consider the impact of irrelevant semantic features, leading to inaccurate semantic representation. To address these challenges, we introduce federated semantic learning and devise FFMSR as our solution. For Limitation 1, we locally learn items’ semantic encodings from their original texts by a multi-layer semantic encoder, and then cluster them on the server to facilitate the transfer of semantic knowledge between domains. To tackle Limitation 2, we integrate both ID and Text modalities on the clients, and utilize them to learn different aspects of items. To handle Limitation 3, a Fast Fourier Transform (FFT)-based filter and a gating mechanism are developed to alleviate the impact of irrelevant semantic information in the local model. We conduct extensive experiments on two real-world datasets, and the results demonstrate the superiority of our FFMSR method over other SOTA methods. Our source codes are publicly available at: https://***/Sapphire-star/FFMSR.

关键词： Cross-domain Recommendation Federated Learning Semantic Fusion Semantic Filter

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Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain Recommendation

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ACM Transactions on Information Systems 1000年

作者： Lei Guo Chenlong Song Feng Guo Xiaohui Han Xiaojun Chang Lei Zhu Shandong Normal University China Liaocheng University China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences) China and Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China Tongji University China

Non-overlapping Cross-domain Sequential Recommendation (NCSR) is the task that focuses on domain knowledge transfer without overlapping entities. Compared with traditional Cross-domain Sequential Recommendation (CSR), NCSR poses several challenges: 1) NCSR methods often rely on explicit item IDs, overlooking semantic information among entities. 2) Existing CSR mainly relies on domain alignment for knowledge transfer, risking semantic loss during alignment. 3) Most previous studies do not consider the many-to-one characteristic, which is challenging because of the utilization of multiple source domains. Given the above challenges, we introduce the prompt learning technique for Many-to-one Non-overlapping Cross-domain Sequential Recommendation (MNCSR) and propose a Text-enhanced Co-attention Prompt Learning Paradigm (TCPLP). Specifically, we capture semantic meanings by representing items through text rather than IDs, leveraging natural language universality to facilitate cross-domain knowledge transfer. Unlike prior works that need to conduct domain alignment, we directly learn transferable domain information, where two types of prompts, i.e., domain-shared and domain-specific prompts, are devised, with a co-attention-based network for prompt encoding. Then, we develop a two-stage learning strategy, i.e., pre-train & prompt-tuning paradigm, for domain knowledge pre-learning and transferring, respectively. We conduct extensive experiments on three datasets and the experimental results demonstrate the superiority of our TCPLP. Our source codes have been publicly released.

关键词： Cross-domain recommendation sequential recommendation non-overlapping cross-domain recommendation

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