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Revocable Registered Attribute-Based Encryption With User Deregistration

IEEE Internet of Things Journal 2025年

作者： Li, Jiguo Chen, Shaobo Lu, Yang Ning, Jianting Shen, Jian Zhang, Yichen Fujian Normal University College of Computer and Cyber Security Fuzhou350117 China Fuzhou350117 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Nanjing Normal University School of Computer and Electronic Information Nanjing210023 China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China

Many businesses are putting their sensitive data in the cloud with the fast growth of cloud computing and storage. To ensure user privacy, it is necessary to keep encrypted data only in the cloud. Attribute-based encryption (ABE) is a popular mean in cloud storage scenarios. ABE is not only faced with key escrow problem but also suffers from user revocation issue when he or she is no longer authorized to access to encrypted data. In order to address these two issues, we propose a revocable registered attribute-based encryption scheme, which not only avoids key escrow problem but also supports precise revocation of a user’s access to a file as well as permanent deregistration of a user from the system. Furthermore, we prove the semantic security of the scheme and conduct a performance experiment to show the efficiency. © 2014 IEEE.

关键词： Sensitive data

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Distributionally robust optimization based chance-constrained energy management for hybrid energy powered cellular networks

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Digital Communications and networks 2023年第3期9卷 797-808页

作者： Pengfei Du Hongjiang Lei Imran Shafique Ansari Jianbo Du Xiaoli Chu The Engineering Research Center of Intelligent Air-ground Integrated Vehicle and Traffic Control Ministry of EducationXihua UniversityChengdu610039China Chongqing Key Lab of Mobile Communications Technology Chongqing University of Posts and TelecommunicationsChongqing400065China James Watt School of Engineering University of GlasgowGlasgowG128QQUK Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and TelecommunicationsXi'an710121China Electronic and Electrical Engineering University of SheffieldSheffieldS102TNUK

Energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emis-sions and electricity expenses of base ***,renewable energy is inherently stochastic and inter-mittent,imposing formidable challenges on reliably satisfying users'time-varying wireless traffic *** addition,the probability distribution of the renewable energy or users’wireless traffic demand is not always fully known in *** this paper,we minimize the total energy cost of a hybrid-energy-powered cellular network by jointly optimizing the energy sharing among base stations,the battery charging and discharging rates,and the energy purchased from the grid under the constraint of a limited battery size at each base *** solving the formulated non-convex chance-constrained stochastic optimization problem,a new ambiguity set is built to characterize the uncertainties in the renewable energy and wireless traffic demands according to interval sets of the mean and *** this ambiguity set,the original optimization problem is transformed into a more tractable second-order cone programming problem by exploiting the distributionally robust optimization ***,a low-complexity distributionally robust chance-constrained energy management algo-rithm,which requires only interval sets of the mean and covariance of stochastic parameters,is *** results of extensive simulation are presented to demonstrate that the proposed algorithm outperforms existing methods in terms of the computational complexity,energy cost,and reliability.

关键词： Cellular networks Energy harvesting Energy management Chance-constrained Distributionally robust optimization

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KGAPG: Knowledge-Aware Neural Group Representation Learning for Attentive Prescription Generation of Traditional Chinese Medicine

KGAPG: Knowledge-Aware Neural Group Representation Learning ...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Li, Shuchen Wang, Wei He, Jieyue MOE Southeast University Key Lab of Computer Network Information Integration Nanjing China

ISBN: (纸本)9781665401265

Prescriptions play an essential role in the process of Traditional Chinese Medicine (TCM) diagnosis and treatment. Prescription generation is to generate a set of herbs to treat the symptoms of a patient by analyzing the relationship between symptoms and herbs. Although there have been a couple of studies to generate prescriptions, they have ignored the implicit relationship between the different symptoms of the patients. In addition, abundant semantic information and interpretability of the knowledge graph can help to portray the complicated relationships between the various modules of TCM. Therefore, this paper proposes a Knowledge-Aware neural Group representation learning model for Attentive Prescription Generation of Traditional Chinese Medicine (KGAPG), which regards the prescription generation task as a group recommendation problem. More specifically, multiple symptoms of a patient are considered as a symptom group and the complicated semantic information between symptoms and herbs can be captured by the knowledge graph. The syndrome information of multiple symptoms which is summarized by a group aggregation method based on the attention mechanism is applied to simulate the actual process of TCM diagnosis and treatment. The experiment results demonstrate that KGAPG is effective on a TCM prescription benchmark dataset, and its evaluation indicators of Precision, Recall and NDCG exceed other state-of-the-art methods. © 2021 IEEE.

关键词： Knowledge graph

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A study of watermarking techniques for data publishing

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Multimedia Tools and Applications 2025年 1-30页

作者： Yuan, Shuguang Yu, Jing Yang, Ke Wang, Yuyang Li, Zhaochen Qiu, Jiabao Yang, Tengfei Chen, Chi Institute of Information Engineering Chinese Academy of Science Beijing China State Key Laboratory of Cyberspace Security Defense Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

In today’s digital era, data sharing is becoming an emerging trend. However, copying, manipulating, and distributing multimedia data over an open channel is easy. Watermarking is one of the most promising methods for copyright protection, tamper detection, and traitor tracing tasks. In the past decades, many diversified watermarking techniques have been created and are still developing, which entails various requirements and challenges. In this paper, we survey recent watermarking techniques on data publishing scenarios. The main contributions of this paper include: 1) We explain the classification methods and background knowledge of watermarking techniques, such as types of function, watermarking process, and basic concepts;2) We primarily survey watermarking techniques for data publishing on different carriers, i.e., relational database, text, print and social media;3) We give representation figures and detailed analysis tables for the essential researches;4) Finally, it provides a discussion of future work according to analysis and observation of existing works. This survey allows researchers, users, and engineers to use efficient watermarking tools to secure published data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Watermarking

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Calibration bottleneck: over-compressed representations are less calibratable 24

Calibration bottleneck: over-compressed representations are ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Deng-Bao Wang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Lab. of Computer Network and Information Integration (Southeast University) MOE China

Although deep neural networks have achieved remarkable success, they often exhibit a significant deficiency in reliable uncertainty calibration. This paper focus on model calibratability, which assesses how amenable a model is to be well recalibrated post-hoc. We find that the widely used weight decay regularizer detrimentally affects model calibratability, subsequently leading to a decline in final calibration performance after post-hoc calibration. To identify the underlying causes leading to poor calibratability, we delve into the calibratability of intermediate features across the hidden layers. We observe a U-shaped trend in the calibratability of intermediate features from the bottom to the top layers, which indicates that over-compression of the top representation layers significantly hinders model calibratability. Based on the observations, this paper introduces a weak classifier hypothesis, i.e., given a weak classification head that has not been over-trained, the representation module can be better learned to produce more calibratable features. Consequently, we propose a progressively layer-peeled training (PLP) method to exploit this hypothesis, thereby enhancing model calibratability. Our comparative experiments show the effectiveness of our method, which improves model calibration and also yields competitive predictive performance.

关键词：

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AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial Attacks

arXiv

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arXiv 2025年

作者： Li, Jin He, Ziqiang Luo, Anwei Hu, Jian-Fang Wang, Z. Jane Kang, Xiangui Guangdong Key Lab of Information Security School of Computer Science and Engineering Sun Yat-Sen University China Electrical and Computer Engineering Dept University of British Columbia Canada

Imperceptible adversarial attacks aim to fool DNNs by adding imperceptible perturbation to the input data. Previous methods typically improve the imperceptibility of attacks by integrating common attack paradigms with specifically designed perception-based losses or the capabilities of generative models. In this paper, we propose Adversarial Attacks in Diffusion (AdvAD), a novel modeling framework distinct from existing attack paradigms. AdvAD innovatively conceptualizes attacking as a non-parametric diffusion process by theoretically exploring basic modeling approach rather than using the denoising or generation abilities of regular diffusion models requiring neural networks. At each step, much subtler yet effective adversarial guidance is crafted using only the attacked model without any additional network, which gradually leads the end of diffusion process from the original image to a desired imperceptible adversarial example. Grounded in a solid theoretical foundation of the proposed non-parametric diffusion process, AdvAD achieves high attack efficacy and imperceptibility with intrinsically lower overall perturbation strength. Additionally, an enhanced version AdvAD-X is proposed to evaluate the extreme of our novel framework under an ideal scenario. Extensive experiments demonstrate the effectiveness of the proposed AdvAD and AdvAD-X. Compared with state-of-the-art imperceptible attacks, AdvAD achieves an average of 99.9% (+17.3%) ASR with 1.34 (-0.97) l2 distance, 49.74 (+4.76) PSNR and 0.9971 (+0.0043) SSIM against four prevalent DNNs with three different architectures on the ImageNet-compatible dataset. Code is available at https://***/XianguiKang/AdvAD. Copyright © 2025, The Authors. All rights reserved.

关键词： HTTP

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Clothed Human Performance Capture with a Double-layer Neural Radiance Fields

Clothed Human Performance Capture with a Double-layer Neural...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition, CVPR 2023

作者： Wang, Kangkan Zhang, Guofeng Cong, Suxu Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. ©2023 IEEE.

关键词： Multilayer neural networks

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Local Spectral for Polarized Communities Search in Attributed Signed network 28th

Local Spectral for Polarized Communities Search in Attribu...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Yang, Fanyi Ma, Huifang Wang, Wentao Li, Zhixin Chang, Liang College of Computer Science and Engineering Northwest Normal University Gansu Lanzhou730070 China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guangxi Guilin541004 China Guangxi Key Lab of Trusted Software Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9783031306747

Signed networks are graphs with edge annotations to indicate whether each interaction is friendly (positive edge) or antagonistic (negative edge). Community search on signed network expects to explore the polarized communities (i.e., two antagonistic subgraphs) containing the set of query nodes. Though previous studies have been proven effective, they generally ignore two insights. First, node attributes provide side information to describe features of nodes. It contributes to optimal results. Secondly, the problem of detecting polarized communities from a global perspective is increasingly limiting and is computationally expensive on large-scale networks. These aspects motivate us to develop a new community search framework searching for Polarized Communities in Attributed Signed network (PCAS). Specifically, we propose a new strategy to combine node attributes with signed topology, which helps to make the most of the different dimensions of information. Furthermore, to search for polarized communities containing the set of query nodes, a sparse indicator-vector is developed based on Rayleigh quotient via solving a linear programming problem. Extensive experimental results on two real-world attributed signed graphs have demonstrated the discovered polarized communities are more accurate and more polarized. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Linear programming

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A generalized reliability analysis of 3-ary n-cubes

A generalized reliability analysis of 3-ary n-cubes

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International Conference on Communication Software and networks, ICCSN

作者： Zefang Bai Guijuan Wang Meijie Ma School of Mathematics and Statistics Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331506322

ISBN: (纸本)9798331506339

Evaluating the reliability of interconnection networks is crucial. The edge-connectivity is a parameter used to assess reliability. In this paper, we discuss a conditional edge-connectivity called the h-extra g-component edge-connectivity. It combines the extra edge-connectivity and the component edge-connectivity. First, this paper proved that $c \lambda_{3}^{h}\left(Q_{n}^{3}\right)=4 n h-$ $4 e_{h}\left(Q_{n}^{3}\right)-h$ for $1 \leq h \leq \frac{1}{2} \cdot 3^{\left\lceil\frac{h}{2}\right\rceil}, n \geq 3$. Then, the paper given the upper bound of h-extra g-component edge-connectivity of the 3 -ary n-cubes for $g \in\left\{4,3^{c}+1\right\}, 1 \leq c \leq\left\lceil\frac{n}{2}\right\rceil-1, n \geq 3$.

关键词： Upper bound Multiprocessor interconnection Software Reliability

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A Non-Intrusive and Real-Time Data Provenance Method for DDS Systems

A Non-Intrusive and Real-Time Data Provenance Method for DDS...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Siyi Wei Jinbin Tu Yun Wang School of Computer Science and Engineering Southeast University Key Lab of Computer Network and Information Integration MOE Nanjing China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

In the era of big data, because of the escalating data security threats, the importance of data provenance is widely recognized. It facilitates the tracking of data origins and evolution, improving the ability to diagnose errors and detect potential malicious attacks within large-scale software systems. However, conventional log-based provenance methods usually collect data intrusively, leading to strong coupling with the system. Meanwhile, although there are some log-based provenance systems that support runtime analysis, they exhibit inadequate real-time analysis performance. This paper proposes a non-intrusive and real-time approach to data provenance in DDS systems. The data capture is achieved with the help of the Data-Centric Publisher-Subscriber model of DDS, thus avoiding source code modification to the existing system. The collected data is then described in PROV-DDS model to generate RDF data. RDF datasets inherently form a directed and labeled graph. The query over RDF datasets is decomposed into start subqueries to speed up processing with star subgraph signature. The experimental results show that our method provenances effectively with less than 1% time overhead and a lower query response time than RDF-3X and Redland.

关键词： Runtime Source coding Data security Stars Software systems Resource description framework Real-time systems

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