Enhancing Personalization and Privacy Management with Support Vector Machines in High Dense Cloud Networks

Cloud computing has changed the way computing is executed. It gives centralized statistics storage and gets entry to software and services, meaning that more companies and individuals can shop and get the right of entry to records from a far-flung region. However, with this improved admission to facts, there may be an ever-developing challenge regarding the personalization and privacy of cloud services. That allows you to ensure the privacy of cloud facts; it is essential to implement green mechanisms for customization and privacy management. Assist Vector Machines (SVMs) have emerged as an effective and efficient customization and privacy protection approach. SVMs provide excessive personalization by training a version with the handiest of the records applicable to the user, which means that SVMs may be used to filter out inappropriate data from the model. Furthermore, SVMs offer an advanced level of privacy safety as the information is anonymized thru the usage of the version. This paper proposes an SVM-based personalization and privateness management machine for excessively dense cloud networks. This system is based on distributed gaining knowledge of, query-primarily based facts choice, and a version-primarily based privateness safety mechanism. The proposed gadget is designed to handle the large amount of data generated in excessively dense cloud networks. Help Vector Machines (SVMs) are a supervised studying method used for category and regression obligations. SVMs are beneficial in highly dense cloud networks, where a massive quantity of customers desires to get the right of entry to equal assets immediately. This paper explores the use of SVMs for reinforcing personalization and privacy control in cloud networks. It proposes a privacy-by using-design method to shield consumer facts from unauthorized access with the aid of SVMs. The proposed method includes two elements: first, using an SVM-based totally method, consumer information may be categorized into different privacy stages consistent with the person's privacy choices. Second, consumer profiles can be used to create customized views of the data, growing the privacy and safety of the user's facts the same time as permitting its efficient get entry. The proposed SVM-based totally method is evaluated on a simulated high-dense cloud community, and its outcomes show that it presents higher privacy and personalization results than traditional strategies..