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Implementation of a Hybrid Triple-Data Encryption Standard and Blowfish Algorithms for Enhancing Image Security in Cloud Environment 云环境下增强图像安全的混合三数据加密标准和河豚算法的实现
Pub Date : 2023-01-01 DOI: 10.4236/jcc.2023.1110009
Mohan Nagamunthala, Ramakrishnan Manjula
In recent years, technological advancements have provided the world with cloud computing which can transfer, store, and process huge data chunks in the form of video, audio, images, and text efficiently. In spite of the universal hype on the subject across the information technology world, protecting sensitive data stored in the cloud server is one of the crucial problems. The large volume and sophistication of cyberattacks conclude to the fact that private pictures need exceptional care than other forms of data on the cloud. Since the user who has stored their private pictures in the cloud has no control over the privacy protection of data, the cloud vendors have to assure a greater level of security in terms of authentication and prevention from cyberattacks. Image encryption algorithms secure visual data by transmuting pictures into an unintelligible form to preserve the confidentiality of pictures over reliable unrestricted social media. This work aims to develop a method for enhancing the security of user photographs on a cloud platform by means of cryptography algorithms. The proposed hybrid technique presents the idea of protecting images in two straightforward steps. First, we generate a chipper text (i.e., secret key) using Triple Data Encryption Standard (TDES) by giving a plaintext and a key as input. Then, the cipher text obtained from TDES is given to the Blowfish algorithm for encrypting the user images. The encrypted image is then uploaded to the database of the cloud server and can be retrieved whenever the user requests it. Both image encryption and decryption processes are analyzed and evaluated based on performance metrics such as cloud storage time, encryption time, decryption time, and encryption throughput. A comparative study with conventional image encryption methods will demonstrate the effectiveness and robustness of our proposed method.
近年来,技术的进步为世界提供了云计算,它可以高效地传输、存储和处理视频、音频、图像和文本形式的巨大数据块。尽管整个信息技术界都在大肆宣传这个话题,但保护存储在云服务器上的敏感数据是关键问题之一。网络攻击的数量之大、技术之复杂表明,与云上其他形式的数据相比,私人图片需要格外小心。由于将私人图片存储在云中的用户无法控制数据的隐私保护,因此云供应商必须在身份验证和防止网络攻击方面确保更高级别的安全性。图像加密算法通过将图片转换为难以理解的形式来保护视觉数据,从而在可靠的不受限制的社交媒体上保护图片的机密性。本工作旨在开发一种通过加密算法增强云平台上用户照片安全性的方法。提出的混合技术提出了两个简单的步骤保护图像的想法。首先,我们通过提供明文和密钥作为输入,使用三重数据加密标准(TDES)生成芯片文本(即密钥)。然后,将从TDES中获得的密文交给Blowfish算法对用户图像进行加密。然后将加密的图像上传到云服务器的数据库,并且可以在用户请求时检索它。基于云存储时间、加密时间、解密时间和加密吞吐量等性能指标,对图像加密和解密过程进行分析和评估。与传统图像加密方法的比较研究将证明我们提出的方法的有效性和鲁棒性。
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引用次数: 0
Improving Resilience Models of Health Systems before COVID-19 Pandemic in Côte d’Ivoire 在COVID-19大流行之前改进科特迪瓦卫生系统的复原力模型
Pub Date : 2023-01-01 DOI: 10.4236/jcc.2023.112001
G. B. N’guessan, Ida Brou Assie, Jean S. Inkpé Haudie
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引用次数: 0
Creating Bengali Freebase Using Wikidata 使用维基数据创建孟加拉语Freebase
Pub Date : 2023-01-01 DOI: 10.4236/jcc.2023.115011
Rukaiya Habib, M. Ferdous, M. Anwar
Freebase is a large collaborative knowledge base and database of general, structured information for public use. Its structured data had been harvested from many sources, including individual, user-submitted wiki contributions. Its aim is to create a global resource so that people (and machines) can access common information more effectively which is mostly available in English. In this research work, we have tried to build the technique of creating the Free-base for Bengali language. Today the number of Bengali articles on the internet is growing day by day. So it has become a necessary to have a structured data store in Bengali. It consists of different types of concepts (topics) and relationships between those topics. These include different types of areas like popular culture (e.g. films, music, books, sports, television), location information (restaurants, geolocations, businesses), scholarly information (linguistics, biology, astronomy), birth place of (poets, politicians, actor, actress) and general knowledge (Wikipedia). It will be much more helpful for relation extraction or any kind of Natural Language Processing (NLP) works on Ben-gali language. In this work, we identified the technique of creating the Bengali Freebase and made a collection of Bengali data. We applied SPARQL query language to extract information from natural language (Bengali) documents such as Wikidata which is typically in RDF (Resource Description Format) triple format.
Freebase是一个大型的协作知识库和数据库,提供一般的、结构化的信息供公众使用。它的结构化数据来自许多来源,包括个人的、用户提交的wiki贡献。它的目标是创建一个全球资源,以便人们(和机器)可以更有效地访问主要以英语提供的公共信息。在本研究工作中,我们尝试建立孟加拉语自由库的创建技术。如今,互联网上的孟加拉文文章数量与日俱增。因此,有一个孟加拉语的结构化数据存储是必要的。它由不同类型的概念(主题)和这些主题之间的关系组成。这些包括不同类型的领域,如流行文化(如电影,音乐,书籍,体育,电视),位置信息(餐馆,地理位置,商业),学术信息(语言学,生物学,天文学),出生地(诗人,政治家,演员,女演员)和一般知识(维基百科)。这对关系提取或任何自然语言处理(NLP)的本加利语工作都有很大的帮助。在这项工作中,我们确定了创建孟加拉语Freebase的技术,并收集了孟加拉语数据。我们应用SPARQL查询语言从自然语言(孟加拉语)文档中提取信息,例如典型的RDF(资源描述格式)三重格式的Wikidata。
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引用次数: 0
Design of Indoor Security Robot based on Robot Operating System 基于机器人操作系统的室内安防机器人设计
Pub Date : 2023-01-01 DOI: 10.4236/jcc.2023.115008
Faxu He, Liye Zhang
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引用次数: 0
End-to-End Auto-Encoder System for Deep Residual Shrinkage Network for AWGN Channels AWGN信道深度残余收缩网络端到端自动编码器系统
Pub Date : 2023-01-01 DOI: 10.4236/jcc.2023.115012
Wenhao Zhao, Shengbo Hu
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引用次数: 1
Neural Network-Based Performance Index Model for Enterprise Goals Simulation and Forecasting 基于神经网络的企业目标仿真与预测绩效指标模型
Pub Date : 2023-01-01 DOI: 10.4236/jcc.2023.118001
J. Essien, Martin Ogharandukun
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引用次数: 0
Deep learning-based emotion detection 基于深度学习的情绪检测
Pub Date : 2022-01-29 DOI: 10.36227/techrxiv.18866159
Yuwei Chen, Jia-Zhou He
Since the deep learning methods used in current face recognition do not balance well between recognition rate and recognition speed, the present work proposed a face expression recognition model based on multilayer feature fusion with lightweight convolutional networks. The model is tested on two commonly used real expression datasets, FER- 2013 and AffectNet, the accuracy of ms_model_M is 74.35% and 56.67%, respectively, and the accuracy of the traditional MovbliNet model is 74.11% and 56.48% in the tests of these two datasets.
鉴于当前人脸识别中使用的深度学习方法在识别率和识别速度之间没有很好的平衡,本文提出了一种基于多层特征融合和轻量级卷积网络的人脸表情识别模型。该模型在FER-2013和AffectNet两个常用的真实表达数据集上进行了测试,在这两个数据集的测试中,ms_model_M的准确率分别为74.35%和56.67%,传统MovbliNet模型的准确率为74.11%和56.48%。
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引用次数: 3
An Improved Genetic Algorithm for UWB Localization 一种改进的超宽带定位遗传算法
Pub Date : 2022-01-01 DOI: 10.4236/jcc.2022.1010001
Xianzhi Zheng
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引用次数: 0
Task-Specific Feature Selection and Detection Algorithms for IoT-Based Networks 基于物联网网络的任务特征选择和检测算法
Pub Date : 2022-01-01 DOI: 10.4236/jcc.2022.1010005
Yang G. Kim, Benito Mendoza, Ohbong Kwon, John Yoon
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引用次数: 2
Preventing Phishing Attack on Voting System Using Visual Cryptography 利用视觉密码防止投票系统的网络钓鱼攻击
Pub Date : 2022-01-01 DOI: 10.4236/jcc.2022.1010010
Ahood Alotaibi, Lama Alhubaidi, Alghala Alyami, Leena A. Marghalani, Bashayer Alharbi, Naya Nagy
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引用次数: 0
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电脑和通信(英文)
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