Quasi‐identifier recognition with echo chamber optimization‐based anonymization for privacy preservation of cloud storage

Abstract

SummaryThe publication of data raises several security concerns, implying that when a reputable company offers data to a third party, personal information is not needed to be revealed. The fundamental issue that led to identity leakage is the connection of quasi‐identifiers (QIDs); however, the majority of researchers overlook the identification of accurate QIDs. This study developed a privacy preservation and clustering‐based quasi‐identification model based on the Echo Chamber optimization with a Z‐mixture parameter for privacy‐preserving combined data publishing to maintain the privacy of the data. The Echo chamber optimization is used to conduct a clustering‐based quasi‐identification to find the significant attributes especially, in determining the dimension of the solutions as well as the precise QIDs using the re‐identification risk rate as the fitness function. The loss of information is greatly reduced in terms of the metrics such as normalized certainty penalty metric, average equivalent class size metric, and discernibility metric. The developed optimized clustering‐based algorithm with the privacy preservation model extensively minimizes the leakage of private information and the utilization of data is well‐maintained compared with other existing algorithms.