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Secure Multi-Party Computation for Collaborative Data Analysis in Network Security 2

Secure Multi-Party Computation for Collaborative Data Analys...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Tamilselvi, P. Lathika, V. Jayachitra, S. Arunkumar, S. Balasubramani, M. Kalaichelvi, V. Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Department of Cse Salem India Psna College of Engineering and Technology Department of Ece Dindigul India Coimbatore Institute of Engineering and Technology Department of Ece Coimbatore India Vsb Engineering College Department of Computer Science and Engineering Karur India Sastra Deemed to Be University Srinivasa Ramanujan Centre Department of Cse Kumbakonam India

ISBN: (纸本)9798350306415

Within the scope of this research, we provide a Secure Multi-Party Computation (SMPC) architecture that has been adapted specifically for the purpose of collaborative data analysis in network protection. Significant improvements have been made to the framework in terms of performance metrics, security, and applicability in the real world. When measured against a baseline, comparative analysis showed a notable reduction of 25% in execution time, a decrease of 20% in communication overhead, and a drop of 16.67% in computational complexity. These enhancements highlight the framework's efficiency advantages, which are vital for maximising responsiveness and scalability in collaborative efforts to improve network security. As a result of the security evaluation, which demonstrates a shift from moderate to high privacy preservation, strong resilience to assaults, and low sensitivity to parameters, the suggested framework has been shown to be reliable. Real-world application scenarios further emphasise its adaptability, showing that its influence on threat intelligence sharing is moderate, while its impact on collaborative incident response is significant, and its impact on vulnerability analysis is low. These findings, when taken as a whole, establish the SMPC framework as a useful resource for organisations that want to improve the effectiveness of their joint efforts to secure networked environments. The research places a strong emphasis on the practical importance of the framework, which helps to build enhanced trust, collaboration, and resilience against developing cyber threats. Assessments of scalability, improvements to cryptographic protocols, and interaction with newly developing technology are some potential future topics for study. advanced threat analysis may also benefit. © 2024 IEEE.

关键词： Collaborative Data Analysis Cryptographic Protocols Network Security Privacy Preservation Secure Multi-Party Computation (SMPC)

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Preface to the 13th Workshop on Bibliometric-enhanced Information Retrieval at ECIR 2023 13

Preface to the 13th Workshop on Bibliometric-enhanced Inform...

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13th International Workshop on Bibliometric-Enhanced Information Retrieval, BIR 2023

作者： Frommholz, Ingo Mayr, Philipp Cabanac, Guillaume Verberne, Suzan Brennan, Jordan School of Engineering Computing and Mathematical Sciences University of Wolverhampton United Kingdom GESIS Leibniz-Institute for the Social Sciences Cologne Germany University of Toulouse Computer Science Department IRIT UMR 5505 France Leiden Institute of Advanced Computer Science Leiden University Netherlands

This preface summarizes the 13th Workshop on Bibliometric-enhanced Information Retrieval (BIR). BIR 2023 was held as hybrid event at April 2nd, 2023, co-located with the 45th European Conference on Information Retriev...

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Boosting Performance and QoS for Concurrent GPU B+trees by Combining-Based Synchronization 23

Boosting Performance and QoS for Concurrent GPU B+trees by C...

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28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Zhang, Weihua Zhao, Chuanlei Peng, Lu Lin, Yuzhe Zhang, Fengzhe Lu, Yunping School of Computer Science Fudan University China Institute of Big Data Fudan University China State Key Laboratory of Mathematical Engineering and Advanced Computing China Parallel Processing Institute Fudan University China Department of Computer Science Tulane University United States

ISBN: (纸本)9798400700156

Concurrent B+trees have been widely used in many systems. With the scale of data requests increasing exponentially, the systems are facing tremendous performance pressure. GPU has shown its potential to accelerate concurrent B+trees performance. When many concurrent requests are processed, the conflicts should be detected and resolved. Prior methods guarantee the correctness of concurrent GPU B+trees through lock-based or software transactional memory (STM)-based approaches. However, these methods complicate the request processing logic, increase the number of memory accesses and bring execution path divergence. They lead to performance degradation and variance in response time increasing. Moreover, previous methods do not guarantee linearizability among concurrent requests. In this paper, we design a combined-based concurrency control framework, called Eirene, for GPU B+tree to reduce the overhead of conflict detection and resolution. First, a combining-based synchronization method is designed to combine and issue requests. It combines the requests with the same key, constructs their dependence, decides the issued request, and determines their return values. Since only one request for each key is issued, key conflicts are eliminated. Then, an optimistic STM method is used to reduce structure conflicts. The query and the update requests are partitioned into different kernels. For the update kernels, STM is involved only when the number of the retry reaches a threshold. Finally, a locality-aware warp reorganization optimization is proposed to improve memory behavior and reduce conflicts by exploiting the locality among requests. Evaluations on an NVIDIA A100 GPU show that Eirene is efficient (a throughput of 2.4 billion per second) and can guarantee linearizability. Compared to the state-of-the-art GPU B+tree, it can achieve a speedup of 7.43X and reduce the response time variance from 36% to 5%. © 2023 ACM.

关键词： Graphics processing unit

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Dynamic Programming Algorithms for Fast and Accurate Cell Lineage Tree Reconstruction from CRISPR-Based Lineage Tracing Data 29th

Dynamic Programming Algorithms for Fast and Accurate Cell ...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Dai, Junyan Molloy, Erin K. Department of Computer Science University of Maryland College Park20742 United States University of Maryland Institute for Advanced Computer Studies University of Maryland College Park20742 United States

ISBN: (纸本)9783031902512

CRISPR-based lineage tracing has emerged as a promising approach for studying cell transformations during development and disease progression. However, the high ratio of cells to mutations, coupled with missing data from silencing or dropout, challenges tree reconstruction, even with the leading method Startle, which is based on Star Homoplasy Parsimony (SHP). Here, we present Star-CDP, the first dynamic programming algorithm that solves the SHP problem within a constrained search space Σ defined by subsets of cells from which a solution cell lineage tree must draw its clades. When Σ is the power set, Star-CDP is an exact exponential algorithm with time complexity O(nm|Σ|2), where n and m denote the number of cells and sites, respectively. We show how to build clade constraints that are polynomially-sized and effective in practice. We also develop algorithms to efficiently count, sample, and build consensus trees from all solutions to the clade-constrained SHP problem, motivated by the challenges in producing consistent phylogenetic signal across the tree with current lineage tracing technologies. In simulations, Star-CDP’s strict consensus typically achieves the best f1-score for the majority of model conditions studied, except when the proportion of missing data from silencing versus dropout reached 100%. Lastly, we analyzed lineage tracing data from a mouse model of lung adenocarcinoma, finding that Star-CDP produced plausible trees, often lowering the number of migration and reseeding events needed to explain metastases compared to Startle. Star-CDP is available on Github: https://***/molloy-lab/Star-CDP. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dynamic programming

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Crime Prediction on Open Data in India Using Data Mining Techniques

Crime Prediction on Open Data in India Using Data Mining Tec...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： M. Menaka P. Sujatha Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai TamilNadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The growth in crime data collection necessitates a deeper theoretical understanding to support practical crime prevention strategies tailored to specific locations and times. Models for forecasting the frequency of various sorts of crimes under an administrative structure of regions utilized by the Indian police, as well as the frequency of anti-social behavior offenses, are investigated in this study. Four algorithms from several techniques are employed. The information is sourced from the Indian police and encompasses over 200,000 records before preprocessing. Based on predictive performance and processing time, the results indicate that SVM may be utilized to forecast crime ***, an ensemble of data mining classification approaches is employed to anticipate crime. Finally, the optimal forecasting technique for achieving the most stable results is presented. The study yields a model that utilizes implicit and explicit data mining approaches to produce credible crime predictions.

关键词： Support vector machines Time-frequency analysis Law enforcement Computational modeling Predictive models Prediction algorithms Data models

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Maestro: A Robust Multi-Head Attention Enhanced CNN Architecture for Heat-Induced Stress Recognition Using EEG Signals 8

Maestro: A Robust Multi-Head Attention Enhanced CNN Architec...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： De, Sagnik Pavuluri, Sreenija Sayyad, Amaan Gupta, Anil Kumar Institute of Radio Physics and Electronics University of Calcutta Kolkata India Department of Computer Science Engineering Gvpce AP Visakhapatnam India Department of Computer Engineering Meswcoe Maharashtra Pune India Hpc I&e Group Centre for Development of Advanced Computing Maharashtra Pune India

ISBN: (纸本)9798331505462

Heat-induced stress impacts various physiological parameters in the body. Elevated temperature can cause tachycardia (an increase in heart rate), as the body attempts to dissipate heat through vasodilation, leading to dehydration and electrolyte imbalances. In addition, the hypothalamus triggers sweating in order to regulate body temperature;that can culminate in fluid and electrolyte loss, which could impact metabolic processes and blood pressure. A prolonged exposure to high temperatures can cause heat stroke, heat exhaustion, and other ailments including organ damage and systemic dysfunction. Existing electroencephalography (EEG)-based heat-induced stress detection often considers the entire EEG frequency range (delta to gamma), concealing redundant and lossy information and increasing the likelihood of false detection rates. To address the limitations of conventional handcrafted feature engineering approaches in heat stress detection, this paper introduces MAESTRO, a novel model comprising two blocks: Convolutional and Multi-head Attention. The Convolutional block extracts precise information from individual EEG frequency bands, while the Multi-head Attention block enhances feature representation through attention mechanism. Finally, two dense layers are employed to classify heat stress into three classes: Acute, Chronic, and Control. The proposed framework undergoes validation using EEG data obtained from 40 rodents in a simulated laboratory environment. The outcomes illustrate the viability of the method in classifying heat-induced stress, yielding remarkable results for overall accuracy, precision, recall, and F1 score of 98.88 %, 98.54 %, 98. 67 %, and 98.60 %, respectively. © 2024 IEEE.

关键词： Blood pressure

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Monitoring Body Mass Index (BMI) Pre & Post Covid-19 Outbreak: A Comprehensive study in Healthcare

Monitoring Body Mass Index (BMI) Pre & Post Covid-19 Outbrea...

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MIT Art, Design and Technology School of computing International Conference (MITADTSoCiCon)

作者： Pritam Ramesh Ahire K. Ulaga Priya Computer Science & Engineering Department Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai

ISBN: (数字)9798350362879

ISBN: (纸本)9798350362886

A comprehensive health monitoring & analyzing system that provides personalized recommendations, goal setting, and tracking based on relevant factors related to BMI & COVID-19. System aims to analyze attack obesity through prediction, prevention and ongoing monitoring through appropriate predictive analytical system. The system's Risk Assessment component categorizes individuals by their risk levels using factors like age, gender, medical history, and behavior related to pre & post covid-19 related to BMI, then relates appropriate connections in human body. The wellness solution includes features such as fitness tracking, data integration, personalized metrics, and predictive analytics to create a holistic approach for predictive analytics health management. The system healthcare monitoring and analyzing system is a groundbreaking solution that leverages predictive analytics to promote a healthy lifestyle, combat obesity, and enhance overall well-being. By considering a range of factors and employing cutting-edge technology, we empower individuals to take control of their health, achieve their fitness objectives, and mitigate health risks.

关键词： COVID-19 Obesity Pandemics Urban areas Sociology Medical services Mental health

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Improved Deepmaxoutmodel for Disease Prediction in Healthcare IoT Cloud System

Improved Deepmaxoutmodel for Disease Prediction in Healthcar...

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IEEE Electron Devices Kolkata Conference (EDKCON)

作者： Preetha P A. Packialatha Department of Computer Science Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu

ISBN: (数字)9798350374643

ISBN: (纸本)9798350374650

The Internet of Medical Things IoMT has grown rapidly due to the growing popularity of wearable technology and its applications in health monitoring systems. The IoMT significantly reduces the death rate by facilitating early sickness identification. Predicting cardiac disease is among the most significant issues with clinical dataset analysis. Finding the key components of heart disease prediction is the goal of the suggested investigation, which makes use of machine learning techniques. Despite several studies on the issue, the accuracy of the findings on the diagnosis of heart illness is low. The purpose of this paper is to provide an innovative framework for healthcare monitoring that includes the following phases. Data preprocessing, feature extraction, acquisition of data, and prediction are the primary stages. The process of acquiring data is at the IoTs layer. The medical data is considered in this case (benchmark datasets of different illnesses). The data pre-processing operations of data cleaning and data filtering at the cloud layer are applied to the data obtained from the IoT sensors (considering the dataset), where the data normalization process will take place. Subsequently, statistical features and raw features are extracted. The multi-layered Deepmaxout model will predict the diseases during in the prediction phase. Lastly, the evaluation of the suggested approach is compared to existing methods using several types of metrics.

关键词： Measurement Heart Filtering Data preprocessing Data acquisition Predictive models Feature extraction Sensors Monitoring Diseases

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Advancements in Speech Recognition: Exploring Feature Extraction Techniques for Enhanced Accuracy and Human-Machine Communication

Advancements in Speech Recognition: Exploring Feature Extrac...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： R. Bhavani Kalaivani Kathirvelu Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

Speech Recognition (SR), for all diverse languages, is still a big challenge. SR is predominantly used in telecommunication, medical documentation, car control, aviation, etc. SR involves breaking down an audio signal of spoken words into discrete units and then matching those units to words in a language model. The main target of SR is to attain healthier verbal communication amid humans and machines. Therefore, it is a good technology of the human-machine crossing point. The SR uses the Feature Extraction (FE) techniques for better accuracy. In this paper, various techniques for FE are discussed, and an overview is provided to assist in selecting the most suitable technique for achieving higher accuracy. The review highlights the importance of developing more accurate and robust SR systems to improve the user experience in various domains. Overall, it provides the manuscript deals with five different techniques, and among them, the Mel-Frequency Cepstral Coefficients (MFCC) technique was found to exhibit a high level of accuracy.

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Empowered Artificial Intelligence Approach using Intuitionistic Fuzzy based Deep Neural Network for Chronic Diseases Prediction

Empowered Artificial Intelligence Approach using Intuitionis...

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Advances in Modern Age Technologies for Health and Engineering science (AMATHE), International Conference on

作者： D. Karthik S. Arun Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Managing the increasing prevalence of chronic illnesses is a major worldwide health concern. It's important to understand the medical problem, its stages, types, and available treatments. The most important component in the early diagnosis of chronic diseases might enhance the diagnostic procedure more successfully. Medical professionals can diagnose a process with the help of numerous artificial intelligence tools. However, ambiguity, irrelevance, and inconsistency in the disease dataset have an impact on the classification accuracy. Conventional machine learning techniques are inadequate for managing scenarios of uncertainty that arise in datasets related to chronic illnesses. To anticipate chronic diseases, each characteristic in the suggested work is represented with a hesitation index. In order to compute the hesitation degree as a more informative procedure in determining the association among features, the belongingness and non-belongingness aspects of the intuitionistic fuzzy concept are induced in the prediction process. This work effectively manages the large chronic illness dataset by developing an intuitionistic fuzzy hesitation index-based similarity measure to weed out unimportant and strongly associated characteristics and enhance the classification model. Utilizing the scored feature information, the deep neural network modifies its hyper parameter to address the overfitting issues that frequently arise in DNNs. The performance of the empowered intuitionistic fuzzy-based deep neural network (EIF-DNN) is analyzed using five distinct chronic conditions, including hypertension, diabetes, hepatitis, cancer, and stroke. Comparing the proposed EIF-DNN against the current versions of multi-layered networks, the simulation results show that it produces promising results in predicting chronic diseases at an early stage.

关键词： Training Uncertainty Accuracy Artificial neural networks Stroke (medical condition) Prediction algorithms Indexes

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