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Optimizing User Data Privacy and Confidentiality in Cloud Storage Systems Through Advanced Obfuscrypt Encryption methods for Enhanced Security and Efficient Data Protection

Optimizing User Data Privacy and Confidentiality in Cloud St...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Ramesh, P.S. Sudha, I. Jagannathan, J. Pandiarajan, Rajkumar Ponmalar, A. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Kalasalingam Academy Of Research and Education School of Computing Department of Information and Technology Krishnankoil Virudhunagar India R.M.K. Engineering College Tamilnadu Chennai India

ISBN: (纸本)9798350388602

With a steady increase in the adoption of cloud storage systems, the client's data are at risk of being leaked. There are several methods of encrypting data which are quite useful but they lack some of the features of today's sophisticated threats. The methodology used in this research propose an encryption framework known as Obfuscrypt to improve the issues of data privacy as well as confidentiality of data stored in cloud storage systems. Obfuscrypt uses conventional encryption strategies incorporated with obfuscation and post-quantum cryptographic algorithms to provide a complex security plan immune to classic and quantum approaches. Some of the proposed other innovations include the decentralised key management with multi-factor authentication and RBAC-ABE as the fine-grained access control techniques. These features make it possible for only the authorized personnel to be in a position to be in a position of decrypting and retrieving the stored data thereby minimizing the possibility of the data being accessed by unauthorized people. The work also considers the issue of performance efficiency of the cloud storage systems. Similar to other symmetric encryption programs, Obfuscrypt uses light encryption algorithms and brings in to play the hardware acceleration so that it may infuse lesser computational overhead in the process of encryption and decryption. As can be clearly seen from the results, the actual work proves that the proposed framework not only offers a higher level of security but does not affect the system performance to a great extent. Also, the study addresses the post-quantum cryptographic algorithm for making the system resistant to an attack that can be posed by quantum computing systems. With the help of these improved algorithms, Obfuscrypt guarantees the permanent data security in the world of constantly emerging cyber threats. © 2024 IEEE.

关键词： Data obfuscation

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Enhancing Healthcare Data Security in Cloud Computing Systems through Advanced Homomorphic Encryption techniques Integrated with Cuckoo Search Optimization Algorithm

Enhancing Healthcare Data Security in Cloud Computing System...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Sudha, I. Ramesh, P.S. Ponmalar, A. Jagannathan, J. Pandiarajan, Rajkumar Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India R.M.K. Engineering College Tamilnadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Kalasalingam Academy of Research and Education School of Computing Department of Information and Technology Krishnankoil Virudhunagar India

ISBN: (纸本)9798350388602

The studies in this research are focused on the mix of homomorphic encryption mechanisms and the Cuckoo Search Optimization Algorithm to promote the security of the healthcare data in the cloud computing system. I have observed that cloud solutions are now at the center of the contemporary health care systems, and security of patient's data remains a top priority. Earlier used encryption techniques, though secure if implemented, have problems in the way that data is to be processed first it has to be decrypted and then made ready for use - it is in this phase that data can be easily hacked into. Homomorphic encryption provides solution to this issue since it enables one to perform computations on encrypted data while maintaining its confidentiality. However, it takes a lot of computation which presents a lot of problems even in terms of simple performance. The Cuckoo Search Algorithm, a new nature-inspired metaheuristic, is incorporated into the encryption process to provide further optimization, whilst preserving the security benefits of the algorithm. The use of the proposed methodology takes place employing Python and its deployment at the cloud platform with elaborated dataset containing anonymized records extracted from electronic health records. The outcome proves that there are great enhancements in encryption and decryption time, computational complexity, and capacity, and thereby making the system appropriate for real-world healthcare solutions. This research offers a strong base for protection of healthcare data in cloud and is useful for the enhancement of adaptive, comprehensive and secure cloud computing systems. © 2024 IEEE.

关键词： Electronic health record

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Deep Transfer Learning model-Based Automated Detection of COVID-19 from X-ray Images and Interpretation of COVID-19 Images Using GLCm Texture Features 6th

Deep Transfer Learning Model-Based Automated Detection of CO...

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6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Ankalaki, Shilpa Shorya, Kartikeya majumdar, Jharna Department of M.Tech CSE Nitte Meenakshi Institute of Technology Bangalore560064 India Department of CSE Nitte Meenakshi Institute of Technology Bangalore560064 India Department of M. Tech Computer Science & Engineering Head Centre for Robotics Research Head Centre for Space Research Nitte Meenakshi Institute of Technology Bangalore560064 India

ISBN: (纸本)9789811613418

The novel coronavirus 2019 (COVID-2019), which initially proved its existence in Wuhan city of China in December 2019, spread quickly around the globe and turned into a pandemic. It has caused a staggering impact on all the sectors of the world like public health, global economy and daily lives. COVID-19 positive cases and death due to COVID-19 are rapidly increasing day by day. It is crucial and essential for fast and accurate automatic detection of COVID-19 infection to make better decisions and to provide appropriate treatment for the patients that can hopefully save their lives. The current has employed the VGG19, RESNET50 and DesNet121 deep learning convolutional neural network with transfer learning to identify and classifies the X-ray images into COVID-19 and non-COVID-19 classes. This study has been extended to analyse the factors which distinguishes COVID-19 and non-COVID-19 images. To accomplish this task, we have employed GLCm features and determined that variance is the best feature for this purpose. GRAD-CAm algorithm has been used to interpret the decision of CNN architecture. In this study, VGG19 and DenseNet121 achieved the classification accuracy of 98.80%, and ResNet50 achieved the accuracy of 97.65% for binary classes (COVID-19 and non-COVID-19 classes). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coronavirus

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Advanced machine Learning Algorithms for 1Predictive Analytics in Healthcare to Enhance Patient Outcomes with Data-Driven Insights

Advanced Machine Learning Algorithms for 1Predictive Analyti...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Pandiarajan, Rajkumar Jagannathan, J. Ramesh, P.S. Ponmalar, A. Sudha School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India R.M.K. Engineering College Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

This research aims to explore the use of modern complex defensive machine learning algorithms in the provision of predictive analytics for health improvement. Incorporating electronic health records, medical image information, and genomic data the work proposes and assesses multiple machine learning models including deep learning, ensemble learning, and reinforcement learning. The main findings shown very high performance in disease prediction, in chronic illness and in the application of efficiency improvements in hospitals as a whole. Overall, such threats as data quality, data privacy, and model interpretability have been discussed in the research and emphasize the importance of the machine learning approach in healthcare. It is highlighted that further development and integration of the interdisciplinary approaches as well as the use of the transparent and interpretable artificial intelligence and machine learning models can be urgent to increase the acceptance among the healthcare staff. This paper enhances the literature on applying machine learning in healthcare systems adding to the repertoire of knowledge for improved patient care. © 2024 IEEE.

关键词： Electronic health record

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Advanced multi-Spectral Image Processing techniques for Enhanced Remote Sensing and Comprehensive Environmental monitoring in Diverse Ecosystems

Advanced Multi-Spectral Image Processing Techniques for Enha...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Jagannathan, J. Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350388602

The research is aimed at improvement of multi-spectral image analysis for remote sensing and monitoring the environment at various ecosystems. A combined spectral imagery encompasses several spectrums at a time compared to single spectral imagery that takes only one spectrum at an instance or at a given area to cover all these spectrums. The study employs digital high resolution satellite images and ground based data to quantify the vegetation vigour, water quality, soil characteristics and changes in land cover. Features, indexes, and classification routines are employed to process the data that is collected with the aid of avionics and other peripherals. This provincial level modelling highlights important gains in environmental status, which includes the general improvement in vegetation condition, water quality as well as physical properties of the soil in the province. This study focuses on the complementary possibilities of integration of multi-spectral imaging with other remote sensing technologies and the high performance of data processing. These techniques play significant roles if used to give current and comprehensive information in the management and conservation of the environment as highlighted in the study. © 2024 IEEE.

关键词： data analysis environmental monitoring image processing techniques machine learning multi-spectral imaging remote sensing soil properties vegetation health water quality

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Enhancing Accessibility for Visually Impaired on Integrating Object, Distance and Face Detection 8

Enhancing Accessibility for Visually Impaired on Integrating...

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8th International Conference on I-SmAC (IoT in Social, mobile, Analytics and Cloud), I-SmAC 2024

作者： Selvarathi, C. Kannan, R. muthumanickam, K. Pandiaraja, P. Venkateswaran, T.S. Arunraj, R. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamilnadu Chennai600 062 India Saranathan College of Engineering Department of Computer Science and Business Systems Tamilnadu Tiruchirapalli620 012 India Kongunadu College of Engineering and Technology Department of Information Technology Tamil Nadu Trichy621215 India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Tamilnadu Karur639113 India K. Ramakrishnan college of Engineering Department of Information Technology Tamilnadu Samayapuram621112 India

ISBN: (纸本)9798350376425

This research presents a novel assistive technology system designed to empower visually impaired individuals by providing enhanced object, facial, and distance detection capabilities. The system aims to replace traditional electronic travel aids with a more comprehensive and cost-effective solution. By combining object and facial recognition with distance detection, the system enables users to navigate their surroundings independently and confidently. Additionally, the system incorporates threat detection capabilities, alerting users to potential dangers and triggering SOS alerts to contacts or authorities. This innovative system offers a more reliable and accessible solution for visually impaired individuals, providing them with the freedom and independence to navigate their daily lives with greater confidence and security. © 2024 IEEE.

关键词： Assistive technology

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A Deep Learning Approach for Automated Skin Disease Classification and Diagnosis 2

A Deep Learning Approach for Automated Skin Disease Classifi...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Suresh, Helina Rajini Amutha, m. Tamizharasi, A. Jaya Krishna, A.P. Lalitha, S. Nithya, P. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sona College of Technology Department of ECE Tamil Nadu Salem India R.M.D. Engineering College Department of Computer Science and Engineering Kavarapettai Tamil Nadu Chennai India Sona College of Technology Department of Electronics and Communication Engineering Tamilnadu Salem India Arunai Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvannamalai India

ISBN: (纸本)9798350317558

Everyone is ware that a wide range of problems affect skin structure, function, and appearance. Dermatological issues or dermatoses are common names used to represent the skin related illness. Early detection of skill llness is possible by employing Artificial Intellengence. A wide range of artificial intelligence techniques have been devised to promptly detect and diagnose skin problems. In this article we are proposing mobilenetv2 and LSTm algorithms to identify skin disorders at an early stage. The combination of mobileNetV2 and LSTm (Long Short-Term memory) for skin disease detection involves leveraging a convolutional neural network (CNN) for image feature extraction and a recurrent neural network (RNN) with LSTm units for sequence modeling, this architecture aims to capture both spatial and temporal dependencies in skin image data, making it well-suited for skin disease detection tasks. The method shown in this study can identify five distinct diseases with ease, and with accuracy rates of 99.2%. © 2024 IEEE.

关键词： Image segmentation

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Design of Proficient Fused mAC Unit for PID Controllers 3

Design of Proficient Fused MAC Unit for PID Controllers

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3rd International Conference on Electrical, Electronics, Information and Communication technologies, ICEEICT 2024

作者： Abinaya, A. maheswari, m. Haridoss, G. Sherin Shibi, C. Babitha Lincy, R. Jency Rubia, J. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India K.Ramakrishnan College of Engineering Department of Ece Tiruchirappalli India M.A.M College of Engineering and Technology Department of Ece Trichy India Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Kattankulathur India Sir Eshwar College of Engineering Department of Computer and Communication Engineering Coimbatore India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

The implementation of PID controllers in ASICs demands a strong grasp of system and hardware design. Despite the straightforward control structure and efficiency of industrial components regulated by PID controllers, they still incur significant power consumption. These control systems incorporate VLSI components like adders, multipliers, and accumulators to operate effectively. In this study, the integration of a fused mAC (multiply-Accumulate) unit is employed for designing PID controllers, aiming to enhance efficiency across power consumption, path delay, and the utilization of LUTs. © 2024 IEEE.

关键词： Three term control systems

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Context-Based Psychosis Emotion Recognition System 1

Context-Based Psychosis Emotion Recognition System

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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Pandi, C. Indra, G. Harini, V. Parthasarathy, C. Vedaraj, m. Rohith Kumar, V. Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India R.M.K College of Engineering and Technology Puduvoyal India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India Rajalakshmi Engineering College Department of Computer Science and Engineering Thandalam India RMD Engineering College Kavaraipettai India

ISBN: (纸本)9798350365337

In the evolving landscape of mental health diagnostics, the identification and understanding of emotional states, particularly in individuals with psychosis, stand as paramount challenges. This research introduces a novel approach termed 'Context-Based Psychosis Emotion Recognition,' aimed at leveraging advanced language models like RoBERTa for refined emotion detection in individuals exhibiting signs of psychosis. By integrating the deep contextual understanding capabilities of RoBERTa with context-based features specific to psychosis, the system achieves heightened accuracy in recognizing and categorizing nuanced emotional states. The methodology incorporates a multi-layered analysis, first extracting raw textual data from patient interactions and then subjecting it to RoBERTa's sophisticated language processing. Subsequent layers of the model are fine-tuned using specialized datasets focusing on psychosis-related emotions. Preliminary results indicate a significant enhancement in emotion recognition accuracy, providing healthcare professionals with invaluable insights into the emotional complexities associated with psychosis. This proj ect not only underscores the potential of transformer-based models in mental health applications but also emphasizes the ethical and privacy considerations essential for deploying such technologies in clinical settings. As a pioneering effort, this research paves the way for advanced diagnostic tools tailored to the intricate emotional landscapes of individuals with psychosis. © 2024 IEEE.

关键词： Emotion Recognition

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Robust Data-Driven Detection of Electricity Theft Adversarial Evasion Attacks in Smart Grids

Robust Data-Driven Detection of Electricity Theft Adversaria...

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作者： Takiddin, Abdulrahman Ismail, muhammad Serpedin, Erchin Texas A&m University Erchin Serpedin Are with the Electrical and Computer Engineering Department College StationTX77843 United States Tennessee Tech University Department of Computer Science CookevilleTN38505 United States

Existing machine learning-based detectors of electricity theft cyberattacks are trained to detect only simple traditional types of cyberattacks while neglecting complex ones like evasion attacks. This paper analyzes the robustness of electricity theft detectors against evasion attacks. Such attacks decrease the reported electricity reading values and fool the electricity theft detectors by injecting adversarial samples. We propose strong evasion attacks that fool the benchmark detectors by iteratively generating adversarial samples based on an electricity reading and its neighboring readings. We study the impact of evasion attacks using white, gray, and black-box settings based on the attacker's knowledge about the detector's parameters or datasets. Our investigations revealed that the performance degradation of benchmark detectors is up to 35.8%, 26.9%, and 22.2% in white, gray, and black-box settings, respectively. To enhance the detection robustness, we propose an ensemble learning-based anomaly detector trained only on benign data to detect unseen attacks (traditional and evasion) by sequentially combining an attentive autoencoder, convolutional-recurrent, and feed forward neural networks. The proposed model offers a stable detection performance where the average degradation is only 0.7 - 3\% , 0.9 - 2.1\% , and 0.4 - 1.7\% in white, gray, and black-box settings, respectively, with maximum adversarial sample injection levels. © 2010-2012 IEEE.

关键词： Electric power transmission networks

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