y Adaptive Weighted Support Vector Machine classification method for privacy preserving in cloud over big data using hadoop framework

Data security is one of the most critical parts of big data investigation. The cloud system applications deal with sensitive information, such as personal, business, or medical records. Threats to this type of data could endanger the cloud systems that store it. On the other hand, traditional security solutions are incapable of safeguarding huge data migration. An effective privacy-preserving system is used to handle the creation of large amounts of data and the security elements of that data throughout the cloud. The information in a cloud-based dataset is initially clustered and balanced using the hadoop framework. The process of clustering is accomplished by Density Peak Weighted Fuzzy C-means Clustering (DPWFCM) algorithm and processed by hadoop framework, which is then encrypted and classified by Enhanced Word Auto Key Encryption (WAKE) and Adaptive Weighted Support Vector Machine with Continuous scatter search (CSS) Optimization Algorithm classifier called as (AWSVM-CSS) technique respectively. The process of encryption and decryption is accomplished in the form of hybrid scheme. The experimental observation of the proposed approach is highly effective in terms of encryption and classification.