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A secure spectrum auction scheme without the trusted party based on the smart contract

Digital Communications and Networks 2021年第2期7卷 223-234页

作者： Jiaqi Wang Ning Lu Qingfeng Cheng Lu Zhou Wenbo Shi School of Computer Science and Engineerings Northeastern University Shenyang 110004China School of Computer Science and Technology Xidian UniversityXi'anChina Strategic Support Force Information Engineering University Zhengzhou450001China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China Division of Computer Science University ofAizuAizu-WakamatsuJapan School of Computer and Communication Engineering Nordieastem UniversityQinhuangdao066004China

With the development of communication 5G networks and technologies,spectrum resources are increasingly *** scarcity of the spectrum resource makes market-driven spectrum auction become an important means of spectrum allocation,and due to the complexity of the network environment,the security of spectrum auctions can not be *** existing secure spectrum auction schemes introduce a semi-honest agent to complete spectrum ***,the hypothetical semi-honest model does not guarantee the security of spectnim auction in the actual application scenario,which may lead to potential security threats:the agent may reveal the privacy of bidders,agent or auctioneer may collude with the bidder to manipulate the spectrum auction,and so *** this paper,a secure spectrum auction scheme without a trusted party is proposed based on the smart contract technology,and the smart contract written into the blockchain replaces the traditional semi-honest agent to cooperate with the auctioneer server to complete the *** order to ensure the security of our scheme,a secure spectrum auction protocol is designed,in which the Software Guard Extensions(SGX)technology and Paillier cryptosystem are used to protect the privacy of *** verification is provided in our protocol by using extensive Pedersen commitment,which prevents the auctioneer server and the bidder from colluding with each other and verifies group bid sum ***,the security analysis is given to propose several types of attacks that can be ***,theoretical analysis and simulation experiments of our protocol are also provided.

关键词： 5g Secure spectrum auction Bidders'bids'privacy Smart contract Public verification

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Replace2Self: Self-Supervised Denoising based on Voxel Replacing and Image Mixing for Diffusion MRI

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Wu, Linhai Wang, Lihui Deng, Zeyu Zhu, Yuemin Wei, Hongjiang Guizhou University Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology 550025 China Univ Lyon INSA Lyon CNRS Inserm CREATIS UMR 5220 LyonF-69621 France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China

Low signal to noise ratio (SNR) remains one of the limitations of diffusion weighted (DW) imaging. How to suppress the influence of noise on the subsequent analysis about the tissue microstructure is still challenging. This work proposed a novel self-supervised learning model, Replace2Self, to effectively reduce spatial correlated noise in DW images. Specifically, a voxel replacement strategy based on similar block matching in Q-space was proposed to destroy the correlations of noise in DW image along one diffusion gradient direction. To alleviate the signal gap caused by the voxel replacement, an image mixing strategy based on complementary mask was designed to generate two different noisy DW images. After that, these two noisy DW images were taken as input, and the non-correlated noisy DWimage after voxel replacement was taken as learning target, a denoising network was trained for denoising. To promote the denoising performance, a complementary mask mixing consistency loss and an inverse replacement regularization loss were also proposed. Through the comparisons against several existing DW image denoising methods on extensive simulation data with different noise distributions, noise levels and b-values, as well as the acquisition datasets and the ablation experiments, we verified the effectiveness of the proposed method. Regardless of the noise distribution and noise level, the proposed method achieved the highest PSNR, which was at least 1.9% higher than the suboptimal method when the noise level reaches 10%. Furthermore, our method has superior generalization ability due to the use of the proposed strategies. © 2025 IEEE. All rights reserved.

关键词： Signal to noise ratio

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Coarse-to-Fine Proposal Refinement Framework for Audio Temporal Forgery Detection and Localization

arXiv

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arXiv 2024年

作者： Wu, Junyan Lu, Wei Luo, Xiangyang Yang, Rui Wang, Qian Cao, Xiaochun School of Computer Science and Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Alibaba Group Hangzhou China School of Cyber Science and Engineering Wuhan University Wuhan China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China

MSC Codes 68T07, 68T10Recently, a novel form of audio partial forgery has posed challenges to its forensics, requiring advanced countermeasures to detect subtle forgery manipulations within long-duration audio. However, existing countermeasures still serve a classification purpose and fail to perform meaningful analysis of the start and end timestamps of partial forgery segments. To address this challenge, we introduce a novel coarse-to-fine proposal refinement framework (CFPRF) that incorporates a frame-level detection network (FDN) and a proposal refinement network (PRN) for audio temporal forgery detection and localization. Specifically, the FDN aims to mine informative inconsistency cues between real and fake frames to obtain discriminative features that are beneficial for roughly indicating forgery regions. The PRN is responsible for predicting confidence scores and regression offsets to refine the coarse-grained proposals derived from the FDN. To learn robust discriminative features, we devise a difference-aware feature learning (DAFL) module guided by contrastive representation learning to enlarge the sensitive differences between different frames induced by minor manipulations. We further design a boundary-aware feature enhancement (BAFE) module to capture the contextual information of multiple transition boundaries and guide the interaction between boundary information and temporal features via a cross-attention mechanism. Extensive experiments show that our CFPRF achieves state-of-the-art performance on various datasets, including LAV-DF, ASVS2019PS, and HAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks

arXiv

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arXiv 2022年

作者： Zhang, Jiawei Wang, Jinwei Wang, Hao Luo, Xiangyang Nanjing University of Information Science and Technology Nanjing China Nanjing University of Science and Technology Nanjing China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhenzhou China

Malicious intelligent algorithms greatly threaten the security of social users’ privacy by detecting and analyzing the uploaded photos to social network platforms. The destruction to DNNs brought by the adversarial attack sparks the potential that adversarial examples serve as a new protection mechanism for privacy security in social networks. However, the existing adversarial example does not have recoverability for serving as an effective protection mechanism. To address this issue, we propose a recoverable generative adversarial network to generate sel f -recouerable aduersarial examples. By modeling the adversarial attack and recovery as a united task, our method can minimize the error of the recovered examples while maximizing the attack ability, resulting in better recoverability of adversarial examples. To further boost the recoverability of these examples, we exploit a dimension reducer to optimize the distribution of adversarial perturbation. The experimental results prove that the adversarial examples generated by the proposed method present superior recoverability, attack ability, and robustness on different datasets and network architectures, which ensure its effectiveness as a protection mechanism in social networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Antisymmetric planar Hall effect in rutile oxide films induced by the Lorentz force

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Science Bulletin 2024年第15期69卷 2362-2369页

作者： Yongwei Cui Zhaoqing Li Haoran Chen Yunzhuo Wu Yue Chen Ke Pei Tong Wu Nian Xie Renchao Che Xuepeng Qiu Yi Liu Zhe Yuan Yizheng Wu Department of Physics State Key Laboratory of Surface PhysicsFudan UniversityShanghai 200433China Institute for Nanoelectronic Devices and Quantum Computing Fudan UniversityShanghai 200433China Interdisciplinary Center for Theoretical Physics and Information Sciences Fudan UniversityShanghai 200433China Center for Advanced Quantum Studies and Department of Physics Beijing Normal UniversityBeijing 100875China Laboratory of Advanced Materials Shanghai Key Lab of Molecular Catalysis and Innovative MaterialsAcademy for Engineering&TechnologyFudan UniversityShanghai 200438China Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and EngineeringTongji UniversityShanghai 200092China Zhejiang Laboratory Hangzhou 311100China Shanghai Research Center for Quantum Sciences Shanghai 201315China Shanghai Key Laboratory of Metasurfaces for Light Manipulation Fudan UniversityShanghai 200433China

The conventional Hall effect is linearly proportional to the field component or magnetization component perpendicular to a film. Despite the increasing theoretical proposals on the Hall effect to the in-plane field or magnetization in various special systems induced by the Berry curvature, such an unconventional Hall effect has only been experimentally reported in Weyl semimetals and in a heterodimensional superlattice. Here, we report an unambiguous experimental observation of the antisymmetric planar Hall effect(APHE) with respect to the in-plane magnetic field in centrosymmetric rutile RuO_(2) and IrO_(2) single-crystal films. The measured Hall resistivity is found to be linearly proportional to the component of the applied in-plane magnetic field along a particular crystal axis and to be independent of the current direction or temperature. Both the experimental observations and theoretical calculations confirm that the APHE in rutile oxide films is induced by the Lorentz force. Our findings can be generalized to ferromagnetic materials for the discovery of anomalous Hall effects and quantum anomalous Hall effects induced by in-plane magnetization. In addition to significantly expanding knowledge of the Hall effect, this work opens the door to explore new members in the Hall effect family.

关键词： Antisymmetric planar Hall effect Lorentz force Rutile oxide films Crystal symmetry

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Efficient Cache Strategy for Face Recognition System 20

Efficient Cache Strategy for Face Recognition System

引用

5th International Conference on Big Data and computing, ICBDC 2020

作者： Zong, Guoxiao Li, Qingbao Zhang, Ping Zhang, Guimin Zhu, Xinbing State Key Laboratory of Mathematical Engineering and Advanced Computing NO.62 Science Avenue ZhengzhouCN 450001 China

ISBN: (纸本)9781450375474

Recently, the need for real-time deep learning applications, such as face recognition system, implement on the embedded devices is increasing. At the system level, caching system is one of the most effective ways to reduce response time. However, embedded front-end devices can hardly guarantee effective system overhead for complex cache system while deploying deep learning model. In this paper, this paper presented an efficient cache strategy for deep learning applications such as face recognition system. This paper first established a distributed embedded system model to describe to functions of the face recognition systems. Then, this paper figures out the goal and idea of optimizing the cache design theoretically. Finally, our cache strategy is experimented and verified better than the traditional methods. © 2020 ACM.

关键词： Internet of things

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H∞Asynchronous Control for Discrete-Time Semi-Markov Jump Systems 16th

H∞Asynchronous Control for Discrete-Time Semi-Markov Jump S...

引用

16th IFAC Symposium on Large Scale Complex Systems: Theory and Applications LSS 2022

作者： Jin, Liqiang Yin, Yanyan Liu, Yanqing Teo, Kok Lay Liu, Fei National Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Institute of Automation Jiangnan University Wuxi214122 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University PerthWA6102 Australia School of Mathematical Sciences Sunway University Kuala Lumpur47500 Malaysia Tianjin University of Finance and Economics Tianjin China

This paper investigates the problem of H∞asynchronous control for discrete-time semi-Markov jump systems. In practice, it is difficult to ensure that controller modes and system modes are updated synchronously. Therefore, a hidden semi-Markov model is introduced to characterize such an asynchronous phenomenon between system modes and controller modes. Meanwhile, the switching signal of the controller is generated by a given conditional probability related to the system mode. Based on the mode-dependent and elapsed-time-dependent Lyapunov function, together with the semi-Markov kernel (SMK) method, sufficient conditions constructed by linear matrix inequalities (LMIs) are derived to ensure the existence of an asynchronous controller such that the resulting system is σ-error mean square stable and satisfies an H∞performance index. Finally, a numerical example is given to illustrate the effectiveness and feasibility of the method proposed. © 2022 Elsevier B.V.. All rights reserved.

关键词： Controllers

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A Vulnerability Detection Method for Intermediate Code Based on a Relational Dependency Graph

A Vulnerability Detection Method for Intermediate Code Based...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Bing Xia Chongjun Tang Wenbo Liu Shihao Chu Yu Dong Dept. ZhongYuan University of Technology Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Dept. ZhongYuan University of Technology Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou China Dept. ZhongYuan University of Technology Zhengzhou China Dept. ZhongYuan University of Technology

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the advancement of information technology, the rapid increase in software systems has led to a growing demand for research on vulnerability detection. In recent years, the fast-paced development of deep learning has introduced new approaches and directions for this field. Existing source code vulnerability detection methods often fail to explicitly preserve the semantic information related to vulnerabilities in the source code, leading to difficulties in extracting vulnerable code features and high false-positive rates in detection. To address these issues, this study proposes a vulnerability detection method for intermediate code based on a relational dependency graph (RDG). After converting the source code into an intermediate representation, it is mapped to an RDG, which accurately expresses the data and control flow dependencies within the program. This approach retains critical node features and refines the types of control flow and data flow edges. By leveraging this method, it becomes possible to trace program data and control dependencies, understand data flow directions, and analyze interactions between different parts of the program, enabling the precise identification and interpretation of potential security vulnerabilities. Drawing on concepts from code property graphs (CPG), this method enhances the understanding and capture of complex relationships between nodes in graph-structured data. By performing vulnerability detection on intermediate code, this approach avoids false positives and negatives caused by language or platform-specific differences, thereby enabling deeper identification and interpretation of complex vulnerability patterns in source code. Experimental results on the SARD dataset demonstrate that the proposed method achieves a high precision rate of 98.7% for detecting command injection vulnerabilities (CWE78), significantly outperforming traditional methods.

关键词： Deep learning Privacy Codes Source coding Semantics Feature extraction Software systems Graph neural networks Security Information technology

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Outsourced secure ID3 decision tree algorithm over horizontally partitioned datasets with consortium blockchain 10

Outsourced secure ID3 decision tree algorithm over horizonta...

引用

10th International Conference on Communication and Network Security, ICCNS 2020

作者： Yang, Ming Hu, Xuexian Wei, Jianghog Zhang, Qihui Liu, Wenfen State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan China Guangxi Key Laboratory of Cryptography and Information Security Guilin Guangxi China

ISBN: (纸本)9781450389037

Due to the capacity of storing massive data and providing huge computing resources, cloud computing has been a desirable platform to assist machine learning in multiple-data-owners scenarios. However, the issue of data privacy is far from being well solved and thus has been a general concern in the cloud-assisted machine learning. For example, in the existing cloud-assisted decision tree classification algorithms, it is very hard to guarantee data privacy since all data owners have to aggregate their data to the cloud platform for model training. In this paper, we investigate the possibility of training a decision tree in the scenario that the distributed data are stored locally in each data owner, where the privacy of the original data can be guaranteed in a more intuitive approach. Specifically, we present a positive answer to the above issue by presenting a privacy-preserving ID3 training scheme using Gini index over horizontally partitioned datasets by multiple data owners. Since each data owner cannot directly divide the local dataset according to the best attributes selected, a consortium blockchain and a homomorphic encryption algorithm are employed to ensure the privacy and usability of the distributed data. Security analysis indicates that our scheme can preserve the privacy of the original data and the intermediate values. Moreover, extensive experiments show that our scheme can achieve the same result compared with the original ID3 decision tree algorithm while additionally preserving data privacy, and calculation time overhead and communication time overhead on data owners decrease greatly. © 2020 ACM.

关键词： Blockchain

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Open Set Recognition for Encrypted Traffic using Intra-class Partition and Boundary Sample Generation

Open Set Recognition for Encrypted Traffic using Intra-class...

引用

IEEE advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Xue Li Jinlong Fei Zan Qi Zhuo Lv Heng Jiang State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Information Engineering University Zhengzhou China National Digital Switching System Engineering Technological Research Center PLA Information Engineering University Zhengzhou China

ISBN: (纸本)9781665479691

Existing deep learning-based encrypted traffic recognition methods can achieve high precision identification performance while protecting user privacy, but almost all of them focus on closed sets, in which training data and test data come from the same label space. However, the real network environment is open, and it is necessary for traffic identification systems to deal with previously unseen categories. To classify known categories at a fine granularity and identify unknown ones, we propose a novel open-set recognition method for encrypted traffic. The decision boundary sample generation method based on constrained Generative Adversarial Networks is presented to generate samples from low-density regions in the known data space. Furthermore, an end-to-end open-set classifier is built using the accurate and diverse simulative samples of unknown classes through intra-class partition and boundary sample generation. Finally, the effectiveness of the proposed method is validated through numerical experiments.

关键词： Data privacy Automation Training data Generative adversarial networks Robustness Information management Cryptography

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