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6th IEEE International Conference on Control Science and Systems engineering, ICCSSE 2020

作者： Xiaoyun, He Hao, Guo Hongsong, Chen Global Energy Interconnection Research Institute Co Ltd State Grid Key Laboratory of Information Network Security Beijing China University of Science and Technology Beijing School of Computer Communication Engineering Beijing China

ISBN: (纸本)9781728198460

Genome-wide association studies (GWAS) have been a popular method for querying Top-k most significant sets of singe-nucleotide polymorphism locations (SNPs) to discover the genetic factors of diseases. Doctors have been increasing interest to in querying SNPs that significantly associated to diseases. However, the queries and GWAS data share service bring great privacy breach risk to the patients. In traditional Laplace privacy preserving mechanism, privacy budget \varepsilonis different to be determined, the Laplace noise is added to original data only once, which causes randomness and potential security risk to privacy protection. A novel approach is proposed to implement the differential privacy preserving for Top-k SNPs query in GWAS. Ensemble Empirical Mode Decomposition(EEMD) is a noise-Assisted data analysis method, it is firstly proposed to be used as noise-Adding approach in privacy protection mechanism. In the EEMD approach, random white noise will be added into the SNPs querying results for many times. Moreover, the parameters of added white noise can be precisely controlled to achieve better effect. PriSTRAT software tool was redeveloped to evaluate EEMD-based privacy preserving approach, the dataset was generated by PLINK tools. Experimental results show that EEMD-based privacy preserving approach has achieved higher accuracy than Laplace mechanism under the same noise-Adding strength. The novel approach could be used to realize differential privacy preserving for Top-k SNPs query in GWAS to achieve the balance between privacy preserving and query accuracy. © 2020 IEEE.

关键词： Empirical mode decomposition

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A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups

arXiv

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arXiv 2022年

作者： Zhao, Chen Zhang, Jialu Hou, Xiaoyue Yeung, Chi Ho Zeng, An College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Shijiazhuang050024 China Department of Science and Environmental Studies The Education University of Hong Kong Hong Kong School of Systems Science Beijing Normal University Beijing100875 China

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence, especially for China where most population has not been infected and most Omicron transmissions are silent. This paper serves as the first study to reveal the complete silent transmission dynamics of COVID-19 overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, retailing, catering and hotel staff are more likely to get infected than other professions. Unlike all other age groups and professions, elderly and retirees are more likely to get infected at home than outside home. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

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Channel Drop Out:A Simple Way to Prevent CNN from Overfitting in Motor Imagery Based BCI

Channel Drop Out:A Simple Way to Prevent CNN from Overfittin...

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2021国际计算机前沿大会

作者： Jing Luo Yaojie Wang Rong Xu Guangming Liu Xiaofan Wang Yijing Gong Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and EngineeringXi'an University of Technology Shaanxi Province Institute of Water Resources and Electric Power Investigation and Design

With the development of deep learning,many motor imagery brain-computer interfaces based on convolutional neural networks(CNNs) show outstanding ***,the trial number of EEG in the training set is usually limited,and redundancy extensively exists in multiple channel ***,overfitting often appears in CNN based motor imagery recognition model and greatly affects the performances of *** this paper,channel drop out is proposed to address this problem by data augmentation and ensemble ***,one of all EEG channels will be dropped and replaced by the mean signal of all EEG *** this way,the trial number in the training set was enlarged by channel drop *** at the testing stage,all the EEG trials processed by channel drop out were fed to the CNN model and the average output probabilities of them were applied to determine the *** experiments were conducted on two popular CNN models applied in motor imagery BCI and BCI Competition IV datasets 2 a to verify the performances of the proposed channel drop out *** results show that average improvements provided by channel drop out in two-category or four-category motor imagery classification are 2.83%and 2.65% compared with the original CNN *** the channel drop out approach significantly improves the performances of motor imagery based BCI.

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Multi-keyword Cipher-text Retrieval Method for Smart Grid Edge Computing 3

Multi-keyword Cipher-text Retrieval Method for Smart Grid Ed...

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2020 3rd International Symposium on Power Electronics and Control engineering, ISPECE 2020

作者： Zhu, Jing Wu, Tao Li, Jintao Liu, Yanbin Jiang, Qixin School of Communication and Information Engineering Chongqing University of Posts and Telecommunications China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science Chongqing University of Posts and Telecommunications Chongqing China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China Electric Power Institute CSG Guangzhou China

With the continuous deepening of China's power grid intelligent upgrading and the rise of edge computing technology, power grid terminals can carry out data storage and intelligent calculation at the edge of the power grid. However, the generated data by users may cause the leakage of user privacy information. The power grid edge system can retrieve the user's electricity data on demand, so as to improve the level of privacy protection and reduce the demand of transmission and computing resources through the localized storage of data. Therefore, how to carry out privacy protection and on-demand retrieval is a key problem to realize the intelligent power grid edge. In this paper, a multi-keyword cipher-text retrieval scheme (MCR- SGEC) for smart grid edge computing is proposed, which can achieve accurate matching of index and trap door, and has low spatial complexity. At the same time, on this basis, a multi-keyword fuzzy cipher-text retrieval method for smart grid edge computing (MFSE-SGEC) is proposed, which allows users to have spelling errors, which makes the user experience better. © Published under licence by IOP Publishing Ltd.

关键词： Edge computing

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Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data

arXiv

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arXiv 2024年

作者： Fang, Congyu Dziedzic, Adam Zhang, Lin Oliva, Laura Verma, Amol Razak, Fahad Papernot, Nicolas Wang, Bo Department of Computer Science University of Toronto Canada Peter Munk Cardiac Centre University Health Network Canada Vector Institute Toronto Canada CISPA Helmholtz Center for Information Security Germany Department of Electrical and Computer Engineering University of Toronto Canada Simon Fraser University Canada St. Michael’s Hospital Unity Health Toronto Canada Department of Medicine University of Toronto Canada Institute of Health Policy Management and Evaluation University of Toronto Canada Department of Laboratory Medicine and Pathobiology Temerty Faculty of Medicine University of Toronto Canada

Background: Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healthcare to achieve better accuracy and generalizability. Sharing data across different healthcare institutions or jurisdictions is challenging because of complex and varying privacy and regulatory requirements. Hence, it is hard but crucial to allow multiple parties to collaboratively train an ML model leveraging the private datasets available at each party without the need for direct sharing of those datasets or compromising the privacy of the datasets through collaboration. Methods: In this paper, we address this challenge by proposing Decentralized, Collaborative, and Privacy-preserving ML for Multi-Hospital Data (DeCaPH). This framework offers the following key benefits: (1) it allows different parties to collaboratively train an ML model without transferring their private datasets (i.e., no data centralization);(2) it safeguards patients’ privacy by limiting the potential privacy leakage arising from any contents shared across the parties during the training process;and (3) it facilitates the ML model training without relying on a centralized party/server. Findings: We demonstrate the generalizability and power of DeCaPH on three distinct tasks using real-world distributed medical datasets: patient mortality prediction using electronic health records, cell-type classification using single-cell human genomes, and pathology identification using chest radiology images. The ML models trained with DeCaPH framework have less than 3.2% drop in model performance comparing to those trained by the non-privacy-preserving collaborative framework. Meanwhile, the average vulnerability to privacy attacks of the models trained with DeCaPH decreased by up to 16%. In addition, models trained with our DeCaPH framework achieve better performance than those models trained solely with the private

关键词： Machine learning

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A Hybrid Blockchain-Based Identity Authentication Scheme for Mobile Crowd Sensing

SSRN

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SSRN 2022年

作者： Taochun, Wang Huimin, Shen Jian, Chen Fulong, Chen Qingshan, Wu Dong, Xie School of Computer and Information Anhui Normal University Wuhu241002 China Anhui Engineering Research Centers of Medical Big Data Intelligent System Anhui Normal University Wuhu241002 China Anhui Key Laboratory of Network and Information Security Anhui Normal University Wuhu241002 China

With the continuous innovation and popularization of mobile smart devices, the application of Mobile Crowd Sensing (MCS) has been widely studied. However, the existing centralized MCS applications that use servers for task publishing and data collection have common problems such as a single point of failure and privacy protection. Based on this, we proposes a hybrid blockchain-based identity authentication scheme for MCS named HBIA, which uses blockchain technology to solve the single point failure problem of MCS. HBIA builds a cluster structure based on factors such as geographical location and balance, and uses this to build a hybrid blockchain, with the cluster head node and the node within the cluster authenticating on the public chain and the private chain respectively, and the use of Zero-knowledge proof(ZKP) to ensure the identity privacy of participants, thus balancing the contradiction between blockchain transparency and participant identity privacy. In addition, HBIA uses zero-knowledge succinct non-interactive argument of knowledge(zk-SNARKs) technology to implement off-chain computing and on-chain verification, further reducing the workload of the blockchain. Finally, the HBIA scheme was tested on the Ethereum public test network Ropsten, and the test results showed that the identity authentication scheme proposed in this paper has advantages in execution time and throughput. © 2022, The Authors. All rights reserved.

关键词： Smart contract

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Vulnerability Detection With Graph Attention network And Metric Learning

TechRxiv

引用

TechRxiv 2022年

作者： Zhang, Chunyong Liu, Bin Fan, Qi Xin, Yang Zhu, Hongliang School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing100876 China College of Information and Network Engineering Anhui Science and Technology University Bengbu233030 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

Static code vulnerability detection is a critical topic in software security. Existing software analysis methods have a high rate of false positives and false negatives. Researchers are interested in employing deep learning to discover vulnerabilities automatically, thanks to the recent success of deep learning algorithms in other application *** paper aims at the problem of insufficient and effective extraction of syntax and semantics, the issue of data imbalance, and the problem of overlapping feature distributions between vulnerable and non-vulnerable samples. We illustrate how to create models in a more principled way. We build GSM, a systematic vulnerability detection model based on Graph Attention network, Sampling methods, and Metric Learning, one phase for one problem solution. When compared to the state-of-the-art approaches, our method achieves 11.5%, 12.3%, 12.57%, and 7.90% improvement in Precision, Recall, F1-Score, and AUC, respectively. Finally, based on the methods proposed in each stage of this paper, we put forward directions and suggestions for more efficient vulnerability detection tasks in the following research. © 2022, CC BY.

关键词： Learning algorithms

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Leakage Reduced Searchable Symmetric Encryption for Multi-keyword Queries

引用

IEEE Transactions on Cloud Computing 2025年

作者： Deng, Qinghua Chen, Lanxiang Zhu, Yizhao Mu, Yi City University of Macau Faculty of Data Science 999078 China Hanshan Normal University School of Computer Information Engineering Guangdong Chaozhou521041 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer Cyber Security Fujian Fuzhou China

Conjunctive keyword queries on untrusted cloud servers represent one of the most common forms of search in encrypted environments. Extensive research has been devoted to developing efficient schemes that support multi-keyword queries. In particular, the Oblivious Cross-Tags (OXT) protocol has received significant attention and is widely regarded as a benchmark in this domain. However, existing schemes fail to simultaneously hide the Keyword-Pair Result Pattern (KPRP) and the conditional Intersection Pattern (IP), potentially leaking additional information to the server. In this work, we propose a novel searchable symmetric encryption (SSE) scheme, referred to as Result Hiding Search (RHS), which aims to minimize result pattern leakage and achieve query result hiding during the index retrieval phase by integrating Private Set Intersection (PSI) techniques. Our scheme enhances privacy by employing PSI for secure membership testing. To improve query efficiency, we shift the expensive complex computation to the offline phase, and utilize efficient pseudorandom functions and hash functions during the online phase. Moreover, we propose a variant of RHS, called vRHS, designed to reduce client-side storage overhead. A simulation-based security proof demonstrates that our scheme is robust against non-adaptive adversaries. Comprehensive experimental evaluation further shows that our approach achieves better security and efficiency trade-offs compared to existing SSE schemes. © 2013 IEEE.

关键词： Hash functions

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YOLO-TP: A process detection model of tunnel real scene based on gradient information interaction

YOLO-TP: A process detection model of tunnel real scene base...

引用

computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Jiaying Fan Shuping Pan Xinbo Jiang Shengtao Zhang Hongliang Liu Chuan Wang Jianghua Li Chenglin Ke Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Technological Innovation Department Shandong Hi-Speed Group Co Ltd JiNan China School of Qilu Transportation Shandong University JiNan China China Railway 14th Bureau Group First Engineering Development Co. Ltd RiZhao China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Target detection technology is an effective method to detect tunnel operation safety. Although the field has made some progress in detection models, the lack of datasets and detection models has hindered its development. We produce and propose Tunnel Processes data (TP), a challenging dataset of high-precision real-world tunnel process target detection that reduces the limitations of research in this field. TP has a high resolution: 1920×1080, 1734images,9 categories. TP is the first tunnel process data set under construction to date, and its fidelity exceeds that of other tunnel process datasets in the field of target detection. In addition, we also propose a YOLO-TP benchmark model to make up for the lack of a high-precision tunnel process image target detection baseline model. Experiments on TP datasets demonstrate their validity, and experiments on the dataset presented in this paper demonstrate the validity of the benchmark model.

关键词： Accuracy Image resolution Computational modeling Object detection Benchmark testing Big Data Feature extraction Data models Safety Artificial intelligence

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EnGAN: Enhancement Generative Adversarial network in Medical Image Segmentation

Research Square

引用

Research Square 2022年

作者： Deng, ErQiang Qin, ZhiGuang Chen, DaJiang Qin, Zhen Ding, Yi Geng, Ji Zhang, Ning Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Jianshe Road SiChuan Chengdu610054 China Department of Electrical and Computer Engineering University of Windsor 401 Sunset Avenue WindsorONN9B 3P4 Canada

Deep learning has been widely used in medical image segmentation, although the accuracy is affected by the problems of small sample space, data imbalance, and cross-device differences. Aiming at such issues, a enhancement GAN network is proposed by using the domain transferring of the adversarial generation network to enhance the original medical images. Specifically, based on retaining the transferability of the original GAN network, a new optimizer is added to generate a sample space with a continuous distribution, which can be used as the target domain of the original image transferring. The optimizer back-propagates the labels of the supervised data set through the segmentation network and maps the discrete distribution of the labels to the continuous image distribution, which has a high similarity to the original image but improves the segmentation efficiency. On this basis, the optimized distribution is taken as the target domain, and the generator and discriminator of the GAN network are trained so that the generator can transfer the original image distribution to the target distribution. extensive experiments are conducted based on MRI, CT, and ultrasound data sets. The experimental results show that, the proposed method has a good generalization effect in medical image segmentation, even when the data set has limited sample space and data imbalance to a certain extent. © 2022, CC BY.

关键词： Generative adversarial networks

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