Techniques for Secure Data Contribution and Retrieval in Social Networks Using Effective Privacy-Preserving Data Mining

Abstract

The major area of data-mining methods that focuses on protecting personal information from unauthorised or unsolicited exposure is called Privacy Preserving Data-Mining (PPDM). The most valuable information is analysed and predicted using data-mining methods. The security of confidential information from unwanted access is at the core of PPDM abstraction. 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 are just a few of the proposed methods in this research work to increase privacy and security (PPHE). To address the immediate difficulties, the proposed SDCRA is first taken into consideration. Based on specifications and application compatibility, the SDCRA algorithm determines a privacy policy and sets up security. The accuracy requirements for numerous datasets may be met by this approach. Online social networks (OSNs) are presently favoured interactive medium for establishing communication, sharing, and disseminating a sizable quantity of data on human existence.