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arXiv 2020年

作者： Hao, Xiaohan Ren, Wei Xiong, Ruoting Zheng, Xianghan Zhu, Tianqing Xiong, Neal N. School of Computer Science China University of Geoscience Wuhan China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Mingbyte Technology QingDao China School of Software University of Technology Sydney UltimoNSW2007 Australia Dept. of Mathematics and Computer Science Northeastern State University United States

Federate learning can conduct machine learning as well as protect the privacy of self-owned training data on corresponding ends, instead of having to upload to a central trusted data aggregation server. In mobile scenarios, a centralized trusted server may not be existing, and even though it exists, the delay will not be manageable, e.g., smart driving cars. Thus, mobile federate learning at the edge with privacy-awareness is attracted more and more attentions. It then imposes a problem - after data are trained on a mobile terminal to obtain a learned model, how to share the model parameters among others to create more accurate and robust accumulative final model. This kind of model sharing confronts several challenges, e.g., the sharing must be conducted without a third trusted party (autonomously), and the sharing must be fair as model training (by training data)is valuable. To tackle the above challenges, we propose a smart contract and IPFS (Inter-Planetary File System) based model sharing protocol and algorithms to address the challenges. The proposed protocol does not rely on a trusted third party, where individual-learned models are shared/stored in corresponding ends. Conducted through extensive experiments, three main steps of the proposed protocol are evaluated. The average executive time of the three steps are 0.059s, 0.060s and 0.032s, demonstrating its efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

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DeepkeyGen: A deep learning-based stream cipher generator for medical image encryption and decryption

arXiv

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arXiv 2020年

作者： Ding, Yi Tan, Fuyuan Qin, Zhen Cao, Mingsheng Choo, Kim-Kwang Raymond Qin, Zhiguang Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China ChengduSichuan610054 China Institute of Electronic and Information Engineering of UESTC in Guangdong Guangdong523808 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China ChengduSichuan610054 China Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX78249-0631 United States

The need for medical image encryption is increasingly pronounced, for example to safeguard the privacy of the patients’ medical imaging data. In this paper, a novel deep learning-based key generation network (DeepkeyGen) is proposed as a stream cipher generator to generate the private key, which can then be used for encrypting and decrypting of medical images. In DeepkeyGen, the generative adversarial network (GAN) is adopted as the learning network to generate the private key. Furthermore, the transformation domain (that represents the "style" of the private key to be generated) is designed to guide the learning network to realize the private key generation process. The goal of DeepkeyGen is to learn the mapping relationship of how to transfer the initial image to the private key. We evaluate DeepkeyGen using three datasets, namely: the Montgomery County chest X-ray dataset, the Ultrasonic Brachial Plexus dataset, and the BraTS18 dataset. The evaluation findings and security analysis show that the proposed key generation network can achieve a high-level security in generating the private key. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Dual attention mechanism object tracking algorithm based on Fully-convolutional Siamese network

Dual attention mechanism object tracking algorithm based on ...

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International Conference on networking and network Applications (NaNA)

作者： Sugang Ma Zixian Zhang Lei Zhang Yanping Chen Xiaobao Yang Lei Pu Zhiqiang Hou School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an Shaanxi China School of Information Engineering Chang’an University Xi’an Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an University of Posts and Telecommunications Xi’an Shaanxi China School of Information and Navigation Air Force Engineering University Xi’an Shaanxi China

In an effort to the problem of insufficient tracking performance of the Fully-convolutional Siamese network (SiamFC) in complex scenarios, a dual attention mechanism object tracking algorithm based on the Fully-convolutional Siamese network is proposed to improve the generalization capability of the tracker by ameliorating the robustness of the template characteristics. Firstly, a global context attention module is appended after the backbone network of SiamFC to ameliorate the power of original feature extraction from two dimensions of spatial and channel. Then, a coordinate attention module is introduced to augment the capability of feature extraction in the channel dimension. Finally, the model of the proposed algorithm is trained on the Got-10k dataset. Five related algorithms are tested on the OTB2015 dataset, the results of experiments manifest that our algorithm outperforms the baseline trackers, the success and precision rate of the proposed algorithm are improved by 3.3% and 6.3%. The average tracking speed is 145FPS, which can demand the requirement of real-time tracking.

关键词： Target tracking Feature extraction Real-time systems Robustness Object tracking Task analysis

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SECP-AKE: Secure and efficient certificateless-password-based authenticated key exchange protocol for smart healthcare systems

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Journal of Systems Architecture 2025年 167卷

作者： Zhiqiang Zhao Xuexian Hu Yining Liu Jianghong Wei Yuanjun Xia Yangfan Liang State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou 450000 Henan China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou 325027 Zhejiang China School of Computer and Information Security Guilin University of Electronic Technology Guilin 541004 Guangxi China College of Information Science and Engineering the Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems the Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province and the Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing 314001 Zhejiang China

Due to the importance and sensitivity of medical data, the security protection and privacy preservation of the Healthcare Internet of Things (IoT) are current research hotspots. However, existing research schemes still suffer from incomplete security properties, imperfect authentication mechanisms, and inadequate privacy preservation. Therefore, this paper presents SECP-AKE, a secure and efficient certificateless-password-based authenticated key exchange protocol for IoT-based smart healthcare, which enables batch authentication, resists physical attacks, and provides strong anonymity. Specifically, using certificateless cryptography, the SECP-AKE protocol enables batch authentication of authorized users and devices while also resolving the key escrow problem. In particular, the SECP-AKE protocol incorporates Physical Unclonable Functions (PUFs) to resist physical attacks, thus enhancing device security and ensuring reliable medical service delivery. Additionally, the design of a pseudonym update mechanism can achieve user unlinkability, thereby providing enhanced privacy preservation. The results from both formal verification using SVO logic and informal security analyses demonstrate that the SECP-AKE protocol is secure and offers more comprehensive security properties. Meanwhile, the use of a well-known automated security verification tool Scyther further evaluates the protocol’s security reliability. Ultimately, comparative experiments on communication overhead and computational overhead demonstrate that the SECP-AKE protocol is efficient and feasible compared to state-of-the-art existing works.

关键词：

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Underwater image enhancement via medium transmission-guided multi-color space embedding

arXiv

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arXiv 2021年

作者： Li, Chongyi Anwar, Saeed Hou, Junhui Cong, Runmin Guo, Chunle Ren, Wenqi CSIRO Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing100044 China College of Computer Science Nankai University Tianjin China Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China

State key laboratory of Information security, Institute of Information Engineering, Chinese Academy of Sciences, China Underwater images suffer from color casts and low contrast due to wavelength- A nd distance-dependent attenuation and scattering. To solve these two degradation issues, we present an underwater image enhancement network via medium transmission-guided multi-color space embedding, called Ucolor. Concretely, we first propose a multi-color space encoder network, which enriches the diversity of feature representations by incorporating the characteristics of different color spaces into a unified structure. Coupled with an attention mechanism, the most discriminative features extracted from multiple color spaces are adaptively integrated and highlighted. Inspired by underwater imaging physical models, we design a medium transmission (indicating the percentage of the scene radiance reaching the camera)-guided decoder network to enhance the response of network towards quality-degraded regions. As a result, our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding and the advantages of both physical model-based and learning-based methods. Extensive experiments demonstrate that our Ucolor achieves superior performance against state-of-the-art methods in terms of both visual quality and quantitative metrics. The code is publicly available at: Https://***/Proj ***. Copyright © 2021, The Authors. All rights reserved.

关键词： Image enhancement

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Unbounded key-Policy Attribute-Based Encryption with Black-Box Traceability

Unbounded Key-Policy Attribute-Based Encryption with Black-B...

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

作者： Yunxiu Ye Zhenfu Cao Jiachen Shen Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen and Shanghai Institute of Intelligent Science and Technology Tongji University China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Attribute-based encryption received widespread attention as soon as it was proposed. However, due to its specific characteristics, some restrictions on attribute set are not flexible enough in actual operation. In addition, since access authorities are determined according to users' attributes, users sharing the same attributes are difficult to be distinguished. Once a malicious user makes illicit gains by their decryption authorities, it is difficult to track down specific user. This paper follows practical demands to propose a more flexible key-policy attribute-based encryption scheme with black-box traceability. The scheme has a constant size of public parameters which can be utilized to construct attribute-related parameters flexibly, and the method of traitor tracing in broadcast encryption is introduced to achieve effective malicious user tracing. In addition, the security and feasibility can be proved by the security proofs and performance evaluation in this paper.

关键词： Performance evaluation Privacy Conferences Encryption

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Practical and Secure Circular Range Search on Private Spatial Data

Practical and Secure Circular Range Search on Private Spatia...

引用

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

作者： Zhihao Zheng Zhenfu Cao Jiachen Shen Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen Shanghai Institute of Intelligent Science and Technology Tongji University China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

With the location-based services (LBS) booming, the volume of spatial data inevitably explodes. In order to reduce local storage and computational overhead, users tend to outsource data and initiate queries to the cloud. However, sensitive data or queries may be compromised if cloud server has access to raw data and plaintext token. To cope with this problem, searchable encryption for geometric range is applied. Geometric range search has wide applications in many scenarios, especially the circular range search. In this paper, a practical and secure circular range search scheme (PSCS) is proposed to support searching for spatial data in a circular range. With our scheme, a semi-honest cloud server will return data for a given circular range correctly without uncovering index privacy or query privacy. We propose a polynomial split algorithm which can decompose the inner product calculation neatly. Then, we define the security of our PSCS formally and prove that it is secure under same-closeness-pattern chosen-plaintext attacks (CLS-CPA) in theory. In addition, we demonstrate the efficiency and accuracy through analysis and experiments compared with existing schemes.

关键词： Data privacy Privacy Search problems Spatial databases Encryption Servers Indexes

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Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation

arXiv

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arXiv 2021年

作者： Peng, Yang Liu, Ping Luo, Yawei Zhou, Pan Xu, Zichuan Liu, Jingen Center for Frontier AI Research Agency for Science Technology and Research Singapore Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Liaoning Dalian China

Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value. In past years, by following the clustering and finetuning paradigm, researchers propose to utilize the teacher-student framework in their methods to decrease the domain gap between different person re-identification datasets. Inspired by recent teacher-student framework based methods, which try to mimic the human learning process either by making the student directly copy behavior from the teacher or selecting reliable learning materials, we propose to conduct further exploration to imitate the human learning process from different aspects, i.e., adaptively updating learning materials, selectively imitating teacher behaviors, and analyzing learning materials structures. The explored three components, collaborate together to constitute a new method for unsupervised domain adaptive person re-identification, which is called Human Learning Imitation framework. The experimental results on three benchmark datasets demonstrate the efficacy of our proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Students

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VCKSCF: Efficient Verifiable Conjunctive keyword Search Based on Cuckoo Filter for Cloud Storage

VCKSCF: Efficient Verifiable Conjunctive Keyword Search Base...

引用

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

作者： Chan Fan Xiaolei Dong Zhenfu Cao Jiachen Shen Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen and Shanghai Institute of Intelligent Science and Technology Tongji University China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Searchable Symmetric Encryption(SSE) remains to be one of the hot topics in the field of cloud storage technology. However, malicious servers may return incorrect search results intentionally, which will bring significant security risks to users. Therefore, verifiable searchable encryption emerged. In the meantime, single-keyword query limits the applications of searchable encryption. Accordingly, more expressive searchable encryption schemes are desirable. In this paper, we propose a verifiable conjunctive keyword search scheme based on Cuckoo filter (VCKSCF), which significantly reduces verification and storage overhead. security analysis indicates that the proposed scheme achieves security in the face of indistinguishability under chosen keyword attack and the unforgeability of proofs and search tokens. Meanwhile, the experimental evaluation demonstrates that it achieves preferable performance in real-world settings.

关键词： Cloud computing Privacy keyword search Filtering algorithms Information filters Encryption Servers

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A Novel Distributed Anomaly Detection Algorithm for Low-density Data

A Novel Distributed Anomaly Detection Algorithm for Low-dens...

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Advances in Electrical Engineering and Computer Applications( AEECA), 2020 IEEE International Conference on

作者： Lin Mei Fengli Zhang University of Electronic Science and Technology of China Chengdu China Southwest Minzu University Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728165219

ISBN: (纸本)9781728165226

Anomaly detection becomes an interesting and significant problem in data mining area. It, in turn, generates many promising applications such as Intrusion Detection, Fraud Detection, Mobile cellular network fraud, and etc. Most of the existing anomaly detection methods are specifically modeled on certain tasks or datasets. And the density-based model has widely applied in anomaly detection. However, for many real-world applications, domain experts usually confront with large scale low-density data. This paper aims to solve the anomaly detection problem of large-scale low-density data. The Relative Local Density-based Outlier Factor (RLDOF) is a highly efficient and promising method for low-density data. While it is not easy to achieve considerable results concerning large-scale data. In this paper, we propose a distributed RLDOF model for low-density data on a large scale. The experimental results indicate that, compared with the existing methods, the model can effectively improve the anomaly detection performance of large-scale low-density data.

关键词： Anomaly detection Computational modeling Resource management Distributed databases Task analysis Processor scheduling

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