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Research on Cloud data Storage Security Privacy Protection System under Digital Campus

Research on Cloud Data Storage Security Privacy Protection S...

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2023 IEEE international conference on Image processing and Computer Applications, ICIPCA 2023

作者： Li, Lingling Cai, Rongsheng Tieling Normal College Liaoning Tieling112000 China City University of Macau Faculty of Data Science Macau999078 China Quanzhou College of Technology Fujian Quanzhou362200 China

ISBN: (纸本)9798350314670

This paper proposes a digital campus cloud data storage security and privacy protection strategy under the Internet of Things environment. Campus users first apply for the attribute key from the trust center and then outsource the encrypted private data to the cloud server. The authorized user generates keyword traps through attribute keys. Only when the attributes of the authorized user meet the specified access control tree, the authorized user is allowed to search the cloud encrypted data through this trap door. A homomorphic encryption algorithm based on ECC is proposed based on the advantages of high security performance and low computational complexity. The algorithm is applied to cloud storage ciphertext processing and cloud computing data aggregation privacy protection. The results show that the scheme cannot only ensure the security of data storage but also protect the identity privacy of campus users. © 2023 IEEE.

关键词： Cloud storage

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A data-driven Predictive Approach Using Custom and Regression algorithms for Estimating Student's Performance

A Data-driven Predictive Approach Using Custom and Regressio...

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2023 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Gangadharan, Rajin Vijayakumar, K. Nithya, P. Siva, T. St. Joseph's Institute of Technology OMR Department of Computer Science and Engineering Chennai India SRM Arts and Science College Kattankulathur Chennai India St. Joseph's Institute of Technology OMR Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350319200

Efficient approaches to estimating and understanding student performance are paramount in the dynamic landscape of higher education. This paper introduces "Adata-driven Predictive ApproachUsing Custom and Regression Models for Estimating Student's Performance,"aiming to provide educators with valuable insights into academic outcomes. By integrating specific academic data sources and employing regression analysis, machine learning algorithms, and other data-driven methodologies, educators can gain actionable insights into student performance trends through predictive modeling. The paper delves into the technical complexities of the strategy, outlining methodologies and presenting practical implementations through illustrative examples and case studies. The ethical considerations of data usage and privacy are carefully investigated, emphasizing responsible implementation in the educational context. Pilot implementations in real-world educational settings are explored, shedding light on the strategy's efficacy and potential impact on educators and students. This study represents a significant advancement in using data-driven strategies to estimate student performance in higher education, envisioning a future where educators are equipped with valuable tools to understand and support each student's academic journey comprehensively. © 2023 IEEE.

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Rethinking data Race Detection in MPI-RMA Programs

Rethinking Data Race Detection in MPI-RMA Programs

引用

2023 international conference on High Performance computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Vinayagame, Radjasouria Saillard, Emmanuelle Thibault, Samuel Nguyen, Van Man Sergent, Marc Eviden Echirolles France University of Bordeaux Bordeaux France Inria Bordeaux France

ISBN: (纸本)9798400707858

Supercomputers are capable of increasingly more computations, and nodes forming them need to communicate even more efficiently with each other. The Message Passing Interface (MPI) proposes a communication model based on one-sided communications called the MPI Remote Memory Access (MPI-RMA). Thanks to these operations, applications can improve the overlap of communications with computations. However, one-sided communications are complex to write since they are subject to data races. This paper rethinks an existing on-the-fly data race detection algorithm for MPI-RMA programs by improving the storage of memory accesses in a Binary Search Tree using a new insertion algorithm based on fragmentation and merging algorithms. Thus, experimental results on real-life applications show that this new insertion algorithm improves the accuracy of the data race detection and can reduce the overhead of the analysis at runtime by a factor up to two. © 2023 ACM.

关键词： Digital storage

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FAST RCNN-Based Implementation of Cloudi: Converting Raw Eye Gaze data into Usable Format 11

FAST RCNN-Based Implementation of Cloudi: Converting Raw Eye...

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11th IEEE international conference on Cloud computing in Emerging Markets, CCEM 2023

作者： Akshay, S. Bharadwaja, Pradyumna J. Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Karnataka Mysuru India

ISBN: (纸本)9798350360059

—Eye-tracking technology has become increasingly popular in various fields such as psychology, human-computer interaction, and market research. However, the process of converting raw eye-tracking data into a usable format for analysis can be complex and time-consuming. This abstract proposes a novel approach to streamline this process by leveraging cloud computing and the Faster R-CNN (FRCNN) algorithm. The proposed method involves three key steps: data preprocessing, cloud-based processing, and FRCNN-based analysis. Cloud computing provides the necessary computational power and storage capabilities to handle large datasets and perform resource-intensive tasks. Finally, the preprocessed eye-tracking data is fed into an FRCNN model for advanced analysis and interpretation. The proposed approach offers several advantages over traditional methods. Firstly, it reduces the burden of data preprocessing by automating the cleaning and formatting tasks. Secondly, the scalability and flexibility of cloud computing enable researchers to analyse large datasets efficiently. Lastly, the integration of FRCNN enhances the analysis capabilities by providing accurate and detailed information about eye movements. ©2023 IEEE.

关键词： Eye movements

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Exploring Cost-Effective Machine Learning Classifiers for Text data: A Literature Review 5th

Exploring Cost-Effective Machine Learning Classifiers for T...

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5th international conference on computing and Network Communications, CoCoNet 2023

作者： Lokman, Abbas Saliimi M.A., Ameedeen N.A., Ghani Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia

ISBN: (纸本)9789819747108

The escalating size of Machine Learning (ML) models in Natural Language processing (NLP) systems presents a challenge for researchers with limited computing resources. This assertion is substantiated by the current research landscape, where major industrial corporations dominate the State-of-The-Art (SoTA) NLP-ML model leaderboard. As substantial ML models entail high computing costs, an economical approach within this research domain is crucial. This paper aims to provide a literature review as a foundational analysis toward exploring the right direction in designing cost-effective or economical NLP-ML models, specifically for text data classification problem. Before delving into a domain-specific NLP-ML review, an introduction to ML algorithms is provided. This introductory section discusses an overview of the most common ML algorithms in recent years, along with the problem domains these algorithms attempt to address, with classification being one of those challenges. The review section comprises three interconnected yet independent subsections: ML for classification, ML for NLP, and the economics of NLP-ML models. Synthesizing information from these review subsections, the subsequent sections present a summary and a future direction. While the summary aims to provide a cohesive conclusion to the entire review, the future direction section includes an example case with a proposed methodology, illustrating where the economical ML model might be implemented. Collectively, these final sections provide a justified direction on how to design an economical NLP-ML classifier, with the hope that it becomes a basic guide for researchers interested in pursuing this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Blockchain-Based Federated Learning-Convolutional Neural Network for Preserving data Privacy and Security

Blockchain-Based Federated Learning-Convolutional Neural Net...

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2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Manoj Kumar, D.P. Ananda Babu, J. Selva Lakshmi, C.B. Fatima, Tabeen Rajesh, N. Kalpataru Institute of Technology Department of Computer Science and Engineering Tiptur India Malnad College of Engineering Department of Information Science and Engineering Hassan India Velammal College of Engineering and Technology Department of Cse India Cmr Technical Campus Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The Internet of Things (IoT) and Industrial cloud computing have completely transferred the healthcare sector due to the rapid rise of distributed healthcare data. The security and privacy of healthcare data are critical issues facing in the healthcare sector. In this research, a BlockChain-based Federated Learning-Convolutional Neural Network (BC-FL-CNN) is proposed for preserving electronic health data privacy by integrating the BC and Deep Learning (DL) approach. CNN is used to classify normal and abnormal users in the processed dataset. The abnormal users were then processed and removed from the database as well as access to the health data utilizing BC with FL approach. Existing methods such as Convolution Neural Network-BlockChain-Cryptography-Federated Learning (CNN-BC-Cryp-FL), FL, and FL Risk-based Authorization Middleware for Healthcare (FRAMH) are used to compare with the proposed BC-FL-CNN approach. The proposed BC-FL-CNN achieves better accuracy of 98.25%, precision of 97.23%, recall of 97.56%, and 97.16% f1-score when compared to the existing approaches like CNN-BC-Cryp-FL, FL, and FRAMH respectively. © 2023 IEEE.

关键词： BlockChain Federated Learning Healthcare data Internet of Things Privacy Security

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A NOVEL APPROACH FOR SECURE STEGANOGRAPHY: INTER-FORMAT CONVERSION COMPENSATION FOR ENHANCED data SECURITY 20

A NOVEL APPROACH FOR SECURE STEGANOGRAPHY: INTER-FORMAT CONV...

引用

20th international conference Applied computing, AC 2023 and the 22nd international conference WWW/Internet, ICWI 2023

作者： Azooz, Hasan Salah, Khawla Ben Kherallah, Monji Naceur, Mohamed Saber Almuthanna University Iraq University of Gafsa Tunisia Faculty of Sciences of Sfax University of Sfax Tunisia National Engineering School of Tunis Tunisia

Steganography research and development are becoming more common in the information society, due to the widespread use of digital media formats and the challenges of managing digital resources and controlling the ownership and use of computer files. At thesame time, addressing steganography challenges is an important issue in the context of the evolving network communication infrastructure for Internet users - participants in open and uncontrolled interaction in the media space. The research concludes that it is necessaryto develop a new qualitative concealing algorithm that enables the hiding of large data. Amounts of data (MB) in still images in commongraphic formats The JPEG format was chosen as the most popular in everyday use scenarios for digital graphics, especially digital images.A qualitative analysis of classes of steganography algorithms focused on embedding data in graphic images for the purpose of covertly transmitting large amounts of data in digital images in JPEG format was carried out, resulting in the development of algorithms for working with digital image structures in JPEG and BMP graphic formats. A mechanism has been developed to compensate for the loss of data bits during inter-format conversions. Additionally, programmatic functions have been established for data insertion and extraction. Machine learning can be used to fine-tune the embedding process, making the resulting stego images harder to decipher for attackers. By usinga machine learning model that has been trained to distinguish between cover and stego images, the embedding process can be fine-tuned tomake the stego image less distinguishable from the original cover image. © ICWI *** rights reserved.

关键词： Steganography

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PolSAR data Classification Using Extreme Gradient Boosting Classifier

PolSAR Data Classification Using Extreme Gradient Boosting C...

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2023 international conference on Earth Observation and Geo-Spatial Information, ICEOGI 2023

作者： Latrache, Houda Ouarzeddine, Mounira Souissi, Boularbah Hammouda, Ikram Crstdla Computational Linguistic Departement Algiers Algeria Laboratory of Image Processing and Radiation Usthb Algiers Algeria

ISBN: (纸本)9798350396720

This work explored the extreme gradient boosting classifier (XGBoost) to classify land cover using PolSAR (Polarimetric Synthetic aperture radar) data. The XGBoost is a type of Ensemble learning algorithms. These algorithms have attracted many researchers in the classification of remote sensing data since they outperform other Machine Learning ML methods in terms of accuracy. In this work, the compensated Kenaugh matrix's diagonal elements and the Six Components Decomposition Method (6SDM) components were combined and used as a feature vector to feed the classifier. ALOS-2 L band data over the San Francisco, USA was used to test the used classifier's performance. The results show that XGBoost outperformed SVM, KNN, and RF classifiers in terms of accuracy. © 2023 IEEE.

关键词： Matrix algebra

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Design of a Graduate Employment Intelligent Recommendation System Based on data Analysis algorithms 3

Design of a Graduate Employment Intelligent Recommendation S...

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3rd international conference on Artificial Intelligence, Big data and algorithms, CAIBDA 2023

作者： Jiang, Xiaolin Wang, Ruiyang School of Cloud Computing Technology and Application Industry Shandong Institute of Commerce and Technology Shandong Jinan China Beijing Normal University-HongKong Baptist University United International College Guangdong Zhuhai China

ISBN: (纸本)9781643684444

In response to the issues of indiscriminate employment and lengthy contract signing time for university graduates, this research investigates the relationship between graduate behavior and job demand. Taking the past graduates of several universities in Hubei province, China as the research subjects, this study proposes an improved algorithm, Apriori-S, based on the Spark computing framework, to enhance the execution efficiency and applicability of the original Apriori algorithm for analyzing complex datasets. The improved algorithm is applied to mine the association rules between past graduate data and employment information. Experimental analysis demonstrates that the improved algorithm has significantly higher execution efficiency compared to the original algorithm and is suitable for analyzing complex datasets with multiple dimensions. Finally, based on the mining results, an 'employment stickiness' model is constructed, and a design for an intelligent job recommendation system for graduates is developed using data analysis algorithms, enabling intelligent job recommendations for employment positions. © 2023 The Authors.

关键词： Employment

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Acconotate: Exploiting Acoustic Changes for Automatic Annotation of Inertial data at the Source 19

Acconotate: Exploiting Acoustic Changes for Automatic Annota...

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19th Annual international conference on Distributed computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Chatterjee, Soumyajit Singh, Arun Mitra, Bivas Chakraborty, Sandip Iit Kharagpur Department of Cse India

ISBN: (纸本)9798350346497

Smart infrastructures often intend to provide personalized context-aware services for their residents. These context-aware services, in turn, often rely on sophisticated machine learning algorithms which need vast volumes of costly annotated sensor data. State-of-the-art automated annotation frameworks try to solve this problem by generating annotated sensor data obtained from personal wearables. However, most of these approaches - (a) either need visual data from the environment or (b) can only work for environments with a single resident. This paper discusses the design of a first-of-its-kind framework Acconotate which can automatically generate annotated data from dual resident smart environments without requiring any visual information. Acconotate achieves this by exploiting the typical transitions present in complex human activities first to solve the critical problem of the user-to-activity association and then use that to annotate the sensor stream available from both the users. Rigorous evaluation with two real-life datasets collected in two diverse scenarios shows that Acconotate can successfully generate annotated sensor data over the edge without human intervention. © 2023 IEEE.

关键词： Learning algorithms

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