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

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 National Biodiversity Future Center NBFC Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral imaging (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 token (CLS token) that is randomly initialized and often struggles to generalize effectively. In this article, 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 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 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 outshones them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Efficient and Revocable Anonymous Account Guarantee System Based on Blockchain 19th

Efficient and Revocable Anonymous Account Guarantee System ...

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19th EAI International Conference on Collaborative Computing: networking, Applications and Worksharing, CollaborateCom 2023

作者： Liang, Weiyou Wang, Yujue Ding, Yong Liang, Hai Yang, Changsong Wang, Huiyong Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Hangzhou Innovation Institute of Beihang University Hangzhou China School of Mathematics and Computing Science GuilinUniversity of Electronic Technology Guilin China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong

ISBN: (纸本)9783031545207

The fast expansion of information technology and public concern for personal privacy and security have raised expectations for the authentication process. Although existing anonymous authentication schemes can achieve anonymous authentication and accountability, they all require users to apply for certificates from the authorization authority, resulting in a significant certificate storage overhead for the authority. Additionally, they have not implemented certificate revocation for anonymous users, which allows malicious users to potentially engage in malicious behavior. Therefore, this paper proposes an efficient and revocable anonymous account guarantee system based on blockchain (ERAAS). The system implements a guarantor mechanism where anonymous users can authenticate their identities through the guarantees provided by guarantors without the need to apply for certificates, reducing the storage overhead of certificates. Furthermore, the system utilizes cryptographic accumulators to enable fast revocation of accounts, preventing malicious users from engaging in further malicious behavior. Moreover, in this system, the certificate authority (CA) can enhance the system’s ability to handle concurrent requests by allocating group keys to the registration authority (RA), authorizing them to register guarantors and sign guarantees. security analysis indicates that the proposed scheme enjoys anonymity, traceability, and revocability and can resist forgery attacks. The experimental comparison demonstrates its practicality. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Authentication

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Code Vulnerability Detection via Nearest Neighbor Mechanism

Code Vulnerability Detection via Nearest Neighbor Mechanism

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Du, Qianjin Kuang, Xiaohui Zhao, Gang Department of Computer Science and Technology Tsinghua University China National Key Laboratory of Science and Technology on Information System Security China

Code vulnerability detection is a fundamental and challenging task in the software security field. Existing research works aim to learn semantic information from the source code by utilizing NLP technologies. However, in vulnerability detection tasks, some vulnerable samples are very similar to non-vulnerable samples, which are difficult to identify. To address this issue and improve detection performance, we introduce the k-nearest neighbor mechanism which retrieves multiple neighbor samples and utilizes label information of retrieved neighbor samples to provide help for model predictions. Besides, we use supervised contrastive learning to make the model learn the discriminative representation and ensure that label information of retrieved neighbor samples is as consistent as possible with the label information of testing samples. Extensive experiments show that our method can achieve obvious performance improvements compared to baseline models. © 2022 Association for Computational Linguistics.

关键词： Semantics

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Gray-scale transformation for traffic classification: leveraging local and global features in flow data 3

Gray-scale transformation for traffic classification: levera...

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3rd International Conference on Electronic information Engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Xia, Yuchao Xue, Mutian Li, Wei Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China University of Electronic Science and Technology of China Chengdu China Sichuan University of Media and Communications Chengdu China

ISBN: (纸本)9781510680449

The rapid expansion and diversification of internet traffic pose a pressing demand for accurate classification of diverse application streams in network management and security monitoring. This paper introduces a deep learning-based method for precise classification of diverse internet application streams in network. By transforming raw traffic data into grayscale images and utilizing Convolutional Neural networks (CNNs) and the Swin-Transformer framework, critical information is extracted at both local and global feature levels. The core of this method is offering a visual representation of the structure and content of traffic data. This visual representation enables subsequent feature extraction and classification. Through experimentation across various applications including email, Skype, YouTube, Vimeo, FTP, and Facebook, the proposed method in traffic classification is demonstrated to be effective. This approach boosts traffic classification accuracy, offers innovation in network management and security monitoring, and holds vital importance in optimizing network performance and security. Additionally, it sets the stage for future research in networking data processing and security methodologies. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： network security

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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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Research on power grid equipment disaster loss model based on "strong wind and heavy rain"combined disaster 2

Research on power grid equipment disaster loss model based o...

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2nd International Conference on Smart Grids and Energy Systems, SGES 2023

作者： Zhang, Zehao Wang, Tianyu Li, Jingshi Feng, Jie Guan, Cheng Yan, Yiran State Grid Key Laboratory of Information & Network Security State Grid Smart Grid Research Institute Co. LTD Beijing102206 China State Grid Corporation of China Beijing100031 China

ISBN: (纸本)9798350371659

This paper comprehensively considers the evolution characteristics and recurrence laws of urban power grid complex disasters, studies the superimposed amplified damage effects of natural disasters of different causes on power grid equipment in the time domain and the space domain, and builds a multi-physical field dynamic simulation model of complex disasters. Aiming at the coupling mechanism of power grid damage under the simultaneous action of multiple physical fields, such as strong wind and rainstorm load, land seepage, flood and waterlogging, the mechanical modeling is carried out to solve the problem of single factor in power grid disaster loss assessment under complex disaster scenarios, and to lay the theoretical and technical foundation for improving the disaster prevention and mitigation ability of power grid equipment in actual complex environments. © 2023 IEEE.

关键词： Disasters

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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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A Data security Protection Method for Deep Neural network Model Based on Mobility and Sharing 1

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Zhao, Xinjian Li, Qianmu Wang, Qi Chen, Shi Li, Tengfei Li, Nianzhe State Grid Jiangsu Information and Telecommunication Company Jiangsu Nanjing210024 China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China State Grid Smart Grid Research Institute Co. Ltd. Jiangsu Nanjing210003 China State Grid Key Laboratory of Information and Network Security Jiangsu Nanjing210003 China

ISBN: (数字)9789819998937

ISBN: (纸本)9789819998920

With the rapid development of digital economy, numerous business scenarios, such as smart grid, energy network, intelligent transportation, etc., require the design and distribution of deep neural network (DNN) models, which typically use large amounts of data and computing resources for training. As a result, DNN models are now considered important assets, but at great risk of being stolen and distributed illegally. In response to the new risks and challenges brought by frequent data flow and sharing, watermarks have been introduced to protect the ownership of DNN models. Watermarks can be extracted in a relatively simple manner to declare ownership of a model. However, watermarks are vulnerable to attacks. In this work, we propose a novel label-based black-box watermark model protection algorithm. Inspired by new labels, we design a method to embed watermarks by adding new labels in the model, and to prevent watermarks from being forged, we use encryption algorithms to generate key samples. We conduct experiments on the VGG19 model using the CIFAR-10 dataset. Experimental results show that the method is robust to fine-tuning attacks, pruning attacks. Furthermore, our method does not affect the performance of deep neural network models. This method helps to solve scenarios such as "internal data circulation, external data sharing and multi-party data collaboration". © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Reversible Data Hiding in Encrypted Images based on Classic McEliece Cryptosystem

Reversible Data Hiding in Encrypted Images based on Classic ...

引用

Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Zongbao Jiang Minqing Zhang Fuqiang Di Weina Dong Key Laboratory of Network and Information Security of PAP Engineering University of PAP Xi’an China

Aiming at the problems that the security of traditional data hiding algorithms is difficult to be guaranteed and the existing algorithms’ embedded volume is restricted under quantum operation attacks, a reversible data hiding algorithm in encrypted images based on Classic McEliece cryptosystem is proposed. The image pixels are first decomposed into bit planes to generate a data stream, and then encrypted in groups using the Classic McEliece cipher algorithm. In the encryption process, the fault tolerance mechanism in its encryption algorithm is utilized to select the appropriate modulation noise composition and the mapping relationship between the secret data, and the error pattern is modulated into noise to interfere with the encryption process, so as to realize the embedding of the secret data. Finally, the marked encrypted image is obtained by using the exclusive-or encryption and scrambling encryption processes. The experimental results show that the algorithm has the reversibility of carrier recovery. Compared with the existing algorithms, this algorithm’s embedded volume is advantageous, and its highest embedding rate is 4.43548 bpp, which realizes large-capacity data hiding while improving security.

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