2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)最新文献

An Efficient Multi-Keyword Search Scheme over Encrypted Data in Multi-Cloud Environment 多云环境下加密数据的高效多关键字搜索方案

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00016

Heng He, Jiaqi Liu, J. Gu, Feng Gao

{"title":"An Efficient Multi-Keyword Search Scheme over Encrypted Data in Multi-Cloud Environment","authors":"Heng He, Jiaqi Liu, J. Gu, Feng Gao","doi":"10.1109/SmartCloud55982.2022.00016","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00016","url":null,"abstract":"Recently multi-cloud has become the main model of cloud computing. With the rapid development of cloud computing technology, users are increasingly concerned about data security in the cloud. To ensure data security, users encrypt private data and upload it to cloud servers. Nevertheless, it is challenging to search ciphertexts with keywords from large amounts of encrypted data of multiple cloud servers. Moreover, existing attribute-based searchable encrypted schemes have several limitations, such as inflexible access control policy, only supporting single or conjunctive keyword search, and low search efficiency. Therefore, we propose an efficient Attribute-based Multi-keyword Search scheme (AMSE) over Encrypted data in multi-cloud environment. AMSE leverages the high-performance Ciphertext-Policy Attribute-Based Encryption (CP-ABE) algorithm to achieve multi-keyword ciphertext search and fine-grained access control. By introducing a retrieval server, AMSE can efficiently and accurately search ciphertexts in multi-cloud. The security analysis and performance evaluation demonstrate that AMSE is secure, highly efficient, and well-suited for multi-cloud.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126646425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Software Source Code Security Audit Algorithm Supporting Incremental Checking 支持增量校验的软件源代码安全审计算法

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00015

Xiuli Li, Guoshi Wang, Chuping Wang, Yan-Fang Qin, Ning Wang

引用次数: 0

Data Generation, Testing and Evaluation of Chinese Natural Language Processing in the Cloud 云环境下汉语自然语言处理的数据生成、测试与评价

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00020

Minjie Ding, Mingang Chen, Wenjie Chen, Lizhi Cai, Yuanhao Chai

{"title":"Data Generation, Testing and Evaluation of Chinese Natural Language Processing in the Cloud","authors":"Minjie Ding, Mingang Chen, Wenjie Chen, Lizhi Cai, Yuanhao Chai","doi":"10.1109/SmartCloud55982.2022.00020","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00020","url":null,"abstract":"With the rapid development of artificial intelligence, natural language processing, as an important branch, has also become a hot research field. A series of super large-scale pre-trained models represented by BERT and GPT have made great progress in natural language understanding and natural language generation, even some of the experimental accuracy exceed the human benchmark. However, these models will also make some mistakes and even fairness problems when they have the language ability equivalent to human beings. In order to verify whether the models can truly understand natural language, the evaluation of these models is particularly important. More methods are needed to evaluate the model. The language model-based evaluation tools often require a lot of computing resources. In this paper, we propose a method for testing and evaluation of Chinese natural language processing in cloud, generate testing data and design tests for Chinese data and test two pre-trained models. The experimental results show that our method can find defects of the model, though it has high performance on specific dataset.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128363880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bandwidth Prediction Method Based on Hybrid LSTM for Content Delivery Network 基于混合LSTM的内容分发网络带宽预测方法

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00040

Xinyu Wang, Xin Du, Wenli Li, Zhihui Lu

{"title":"A Bandwidth Prediction Method Based on Hybrid LSTM for Content Delivery Network","authors":"Xinyu Wang, Xin Du, Wenli Li, Zhihui Lu","doi":"10.1109/SmartCloud55982.2022.00040","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00040","url":null,"abstract":"Content Delivery Network (CDN) can store workflow content by deploying edge service nodes and realizing network optimization with reasonable scheduling strategies. Manual selection of appropriate service clusters is coupled with low timeliness and a high economic burden. Therefore, the timing prediction of bandwidth remains a persistent demand to choose service clusters that can take on a safe workload. In this study, we proposes a model which can predict the load level in the future period to optimize the content delivery network. This model uses a machine learning method (K-means) for data optimization, which discarded 3114 of 185572 pieces of data that impact subsequent prediction models. Afterward, the model uses a 4-layers Long Short-Term Memory Network(LSTM) to predict the aggregated temporal data. The model named BK-LSTM considers the execution time and accuracy, eventually learning the real-time bandwidth demand pattern of 654 servers in the specific cluster. Experiments show that our BK-LSTM model has a mean absolute percentage error(MAPE) metric of about 15.2% on the test set, demonstrating this model’s ability to predict bandwidth workload well.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124699736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Overview of Medical Data Privacy Protection based on Blockchain Technology 基于区块链技术的医疗数据隐私保护综述

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00039

Lin Chen, Qingchun Yu, W. Liang, Jiahong Cai, Hangyu Zhu, Songyou Xie

引用次数: 2

Detecting and Warning Abnormal Transaction of Virtual Cryptocurrency Based on Privacy Protection Framework 基于隐私保护框架的虚拟货币异常交易检测与预警

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00018

Tong Zhu, Chenyang Liao, Lanting Guo, Ziyang Zhou, Wenwen Ruan, Wenhao Wang, Xinyu Li, Qingfu Zhang, Hao Zheng, Shuang Wang, Yuetong Liu

{"title":"Detecting and Warning Abnormal Transaction of Virtual Cryptocurrency Based on Privacy Protection Framework","authors":"Tong Zhu, Chenyang Liao, Lanting Guo, Ziyang Zhou, Wenwen Ruan, Wenhao Wang, Xinyu Li, Qingfu Zhang, Hao Zheng, Shuang Wang, Yuetong Liu","doi":"10.1109/SmartCloud55982.2022.00018","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00018","url":null,"abstract":"For detecting and warning abnormal transaction of virtual cryptocurrency: we proposed PROTECTION (PRivacy-preserving suspiciOus Transaction detECTION), and proposed big matrix inversion algorithm to solve the problem that the physics of TEE is easily limited by memory size. Based on the privacy protection framework, we proposed three supervised learning algorithms to detect and warn abnormal transactions, they respectively are the federated logistic regression model(VERTIGO) over vertically partitioned data, the federated random forest model over vertically partitioned data, and the federated multilayer perceptron model over vertically partitioned data. According to the experimental results, we found that among the three algorithms, the federated logistic regression model(VERTIGO) over vertically partitioned data is ahead of the federated random forest model over vertically partitioned data, and the federated multilayer perceptron model over vertically partitioned data in all indicators, it has a good effect on detecting abnormal transaction of virtual cryptocurrency.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126777688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

niDts: A New Generation Intelligent Data Trading System niDts:新一代智能数据交易系统

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00030

Qifeng Tang, Zhiqing Shao, Lihua Huang, Hsunfang Cho, Yiguang Zhang

引用次数: 0

Transwarp ArgoDB: A Distributed Flash Database Transwarp ArgoDB:一个分布式Flash数据库

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00043

Changchun Zhang, Cheng Lv, Zhenqiang Chen, Yucheng Lu, Yuxuan Tian, Jiabao Wu, Hongshan Yang

引用次数: 0

UCloudStack - A Private Cloud Platform for Lightweight Delivery UCloudStack——一个轻量级交付的私有云平台

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/SmartCloud55982.2022.00036

X. Peng, Yilun Song, Kangjian Yuan, Xu Guo, Zhihui Lu, Jie Liu

引用次数: 0

Night Fatigue Driving Detection Algorithm based on Lightweight Zero-DCE 基于轻量级零- dce的夜间疲劳驾驶检测算法

2022 IEEE 7th International Conference on Smart Cloud (SmartCloud) Pub Date : 2022-10-01 DOI: 10.1109/smartcloud55982.2022.00028

ZhanTi Ll, Ni Jia, Hongmei Jin

{"title":"Night Fatigue Driving Detection Algorithm based on Lightweight Zero-DCE","authors":"ZhanTi Ll, Ni Jia, Hongmei Jin","doi":"10.1109/smartcloud55982.2022.00028","DOIUrl":"https://doi.org/10.1109/smartcloud55982.2022.00028","url":null,"abstract":"Aiming at the problem of low image exposure in low-light scenes at night, resulting in low accuracy of fatigue driving detection, a lightweight Zero-DCE night fatigue driving detection algorithm was proposed The depthwise separable convolution is used in the backbone feature extraction nebv0rk of the Zero-DCE model to improve the speed of the detection nebv0rk and reduce the amount of nebv0rk parameters; the down-sampled input is used as the input of the enhanced nebv0rk, and the output is mapped back to the original resolution by up-sampling. Perf0rm image enhancement, effectively balancing enhancement performance and significantly reducing computational cost. The facial eye and mouth features are detected by the target detection algorithm and the open and closed states are identified and the detection results are calculated and output according to the eye and mouth fatigue parameters combined with the threshold The experimental results show that in the low-light environment at night, the detection algorithm proposed in this paper improves the detection accuracy by 17.07% compared with the existing algorithm, and the detection time after algorithm fusion is 0.012s, which is more in line with the application requirements of fatigue driving detection scenarios.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129582806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0