Improving Privacy Preservation Approach for Healthcare Data using Frequency Distribution of Delicate Information

Abstract

In the modern world, everyone wishes that their personal information wouldn't be made public in any manner. In order to keep personal information hidden from prying eyes, privacy protection is essential. The data may be in the form of big data and minimization of risk and protection of sensitive data is important. In this research, a revolutionary customized privacy-preserving method is implemented that addresses the drawbacks of earlier personalized privacy as well as other anonymization methods. There are two main components that make up the proposed method's core. Delicate Information and Delicate Weight are two additional attributes which are used in the record table, are covered in the first section. The record holder's Delicate Information (DI) decides whether or not secrecy should be kept or if it should be shared. How delicate an attribute value is compared to the rest is indicated by its Delicate weight (DW). The second part covers a new representation used for anonymization termed the Frequency Distribution Block (FDB) and Quasi-Identifier Distribution Block (QIDB). According to experimental findings, the proposed system executes more quickly and with less data loss than current approaches.

Implementation of Big Data Privacy Preservation Technique for Electronic Health Records in Multivendor Environment

“…This paper presents a novel integrated architecture for collecting clinical data from heterogeneous sources and transforming them into formats useful for clinical secondary use while adhering to the above requirements. Proper distribution of attribute weightage is also important [7].…”