Secure Data-Contribution Retrieval Algorithm for Preserving the Privacy of Credential Data in a Web Environment

Abstract

The primary domain of data-mining techniques that concentrates on the protection of confidential information from unsanctioned or unsolicited disclosure is Privacy Preserving Data-Mining (PPDM). The information that is most beneficial is analyzed and predicted using data mining techniques. The abstraction of PPDM is inherently concerned with safeguarding confidential information from unauthorized access. This research work encompasses a variety of proposed methods to enhance privacy and security, including the Secure Data Contribution Retrieval Algorithm (SDCRA), Enhanced-Attribute Based Encryption (E-ABE), Level by Level Security Optimization and Content Visualization (LSOCV) algorithm, and Privacy Preserved Hadoop Environment (PPHE). Initially, the proposed SDCRA is taken into account in order to resolve the existing concerns. This SDCRA algorithm establishes a privacy policy and organizes security in accordance with the compatibility and requirements of applications. This algorithm is capable of satisfying the accuracy constraints for numerous datasets. Currently, online social networks (OSNs) are a frequently employed interactive medium that is designed to facilitate the sharing, dissemination, and communication of a substantial amount of human life data. An experimental analysis employs the input data from social datasets.