Osns Secure Content Contribution And By Reusing Step By Step Security, Optimization And Visualization Algorithm

Abstract

Privacy-Preserving Data Mining is the main area of data-mining technology. It focuses on protecting confidential information against unauthorized or unwelcome disclosure. Data mining techniques can identify and predict the most valuable information. PPDM abstraction is concerned with the protection of secret communications from unauthorized access. There are many methods that can be used for privacy and security enhancements. These include Secure Data Contribution Retrieval Algorithms and Enhanced Attribute-Based Encryption, Level by-level Security Optimization, Content Visualization, and Level-by-level Encryption. The current problems are addressed by the proposed SDCRA. The SDCRA algorithm creates privacy policies. The security is then organized according to compatibility and needs. This algorithm can be used to meet accuracy requirements for multiple datasets. Online Social Networks are popular interactive media that allows users to communicate, share and disseminate large amounts of human life information. Users will be able to share data content across OSNs using the proposed enhanced ABE approach. Data leakage can occur during the data storage phase if sensitive data is collected and stored in an insecure environment. The third benefit of the SSOV algorithm's privacy protection is data visualization and content sharing. After assessing the OSN's privacy compatibility, they adjust privacy levels to meet user needs in social networks. Fourth, the PPHE framework determines whether tweets contain confidential or private information. It applies data suppression techniques to tweets that have been identified as private. An experimental analysis is performed using input data from a social dataset.