[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-291013-105":59,"doc-detail-291013-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","machine-learning-algorithms-for-privacy-preserving-in-vehicular-ad-hoc-network","Machine learning algorithms for privacy preserving in vehicular ad hoc network","","Machine learning algorithms are evaluated for achieving privacy preservation in vehicular ad hoc networks (VANETs). The approach generates two distinct group keys for prime and secondary users, using roadside units (RSUs) to broadcast one key from the trusted authority (TA) and enabling secondary users to transmit the other key. Naïve Bayesian classifier, SVM, KNN, ANN, and Bayesian network methods are compared alongside a proposed deep neural network enhanced with black widow optimization (DNN-BWO). Results are assessed using delay, network lifetime, throughput, delivery ratio, and drop, showing improved performance over existing methods.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-algorithms-for-privacy-preserving-in-vehicular-ad-hoc-network/291013/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-algorithms-for-privacy-preserving-in-vehicular-ad-hoc-network/291013.png","ImageObject",300,407,{"name":92,"@type":93},"Evangeline","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main goal of the proposed VANET privacy approach?","Question",{"text":113,"@type":114},"The work aims to enhance privacy in vehicular ad hoc networks and avoid vulnerable attacks through a privacy-preserving key and authentication mechanism.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How are group keys generated and distributed in the method?",{"text":118,"@type":114},"Two distinct group keys are created for prime and secondary users: RSUs broadcast one key from the trusted authority to primary users, while secondary users transmit the other key.",{"name":120,"@type":111,"acceptedAnswer":121},"Which algorithms and models are used for learning and protection?",{"text":122,"@type":114},"The paper uses Naïve Bayesian classifier, SVM, KNN, ANN, Bayesian network, and compares them with a proposed deep neural network protected by black widow optimization (BWO) for weight optimization.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291013,1789643180,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":46,"preview_page_count":4},13056703019662,"https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188","Machine learning algorithms for privacy preserving in vehicular ad hoc network  \nShazia Sulthana, Byppanahalli Narayana Reddy Manjunatha Reddy  \nDepartment of Electronics and Communication, Global Academy of Technology, Visvesvaraya Technological University,  \nBelagavi, India  \n\n| Article history:\u003Cbr>Received Jul 2, 2022 Revised Nov 7, 2022 Accepted Nov 18, 2022 | Machine learning (ML) will improve the outcomes through the use of methods that categorize the information into the predetermined set. This work is to present an estimation and assessment of machine learning techniques for achieving privacy preservation in vehicular ad hoc networks (VANETs) . This method generates two distinct group keys for prime and secondary users. Roadside units (RSUs) are deployed to broadcast one group key from the trusted authority (TA) to the primary users, and secondary users are utilized to transmit the other group key. The main aim of this network is developed to avoid vulnerable attacks and to enhance the privacy of this network, Naïve Bayesian classifier (BC), support vector machine (SVM), K-nearest neighbor (KNN), artificial neural networks (ANN), Bayesian network (BN) methods are utilized in correlation with the proposed deep neural networks (DNN) with the black widow optimization (BWO) for protection preserving. These learning characterization procedures are assessed concerning delay, network lifetime, throughput, delivery ratio, and drop and this proposed calculation (DNN-BWO) shows improved results than the current methodologies.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr>|\n| --- | --- |\n| Keywords:\u003Cbr>Black widow optimization Deep neural network Privacy\u003Cbr>Trusted authority Vehicular ad hoc networks |  |\n\nCorresponding Author:  \nShazia Sulthana  \nDepartment of Electronics and Communication, Global Academy of Technology Visvesvaraya Technological University  \nBelagavi, Karnataka, India  \nEmail: [shaziasulthana14@gmail.com](shaziasulthana14@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nVehicular ad hoc networks (VANET) plays a major key role in intelligent transportation systems (ITS), as information is generated and exchanged wirelessly through vehicles and other devices. There are various communication occurs from one unit to another unit. The VANET architecture is shown in Figure 1. In vehicle communications that receive internal system data or vehicle operation, one determines factors such as driver fatigue, and drowsiness. Determination of those factors as well as their quality is critical to public safety and driver safety. As technological development is changing, the world experiencing today is touching in most areas, particularly in the field of communication. Since open-space technology has advanced, the issues with traffic congestion and accidents on the roads have been only partially resolved. Researchers started working with this technology that communicates vehicle to vehicle to develop direct or indirect links between them and to be dedicated to anticipated critical problems such as forthcoming incidents or traffic congestion in order to alleviate privacy issues. Deep neural networks which is a machine learning technique have become an essential tool for an extensive range of applications such as the classification of images, pattern recognition, or user language conversion. These techniques have molded extremely high accuracy of prediction in comparison with human performance. Techniques for interpreting and perceptive of the model have become a key ingredient of a strong corroboration method. Safety, infotainment, and multimedia data are tiled in an open-  \naccess environment through the VANET. Serious traffic accidents could result from a malicious vehicle interfering with a network or from data transmission errors. To provide the privacy of data transmission in an open environment, the authentication of vehicles is primary in VANET. The objective of the work:  \n− In this work, a messa","cbCaighLxa48s3x3","https://ap.wps.com/l/cbCaighLxa48s3x3","pdf",412283,"English","# Article Info ABSTRACT\n# 1. INTRODUCTION\n# VANET Architecture and Communication Context\n# Objective and Proposed Method\n# Evaluation Metrics\n# Related Work","[{\"question\":\"What is the main goal of the proposed VANET privacy approach?\",\"answer\":\"The work aims to enhance privacy in vehicular ad hoc networks and avoid vulnerable attacks through a privacy-preserving key and authentication mechanism.\"},{\"question\":\"How are group keys generated and distributed in the method?\",\"answer\":\"Two distinct group keys are created for prime and secondary users: RSUs broadcast one key from the trusted authority to primary users, while secondary users transmit the other key.\"},{\"question\":\"Which algorithms and models are used for learning and protection?\",\"answer\":\"The paper uses Naïve Bayesian classifier, SVM, KNN, ANN, Bayesian network, and compares them with a proposed deep neural network protected by black widow optimization (BWO) for weight optimization.\"}]","Machine learning algorithms for privacy preserving in vehicular ad hoc network | PDF"]