Abstract In this era of big data, seamless distribution of healthcare information is crucial for improving patient care and advancing medical research, necessitating meticulous attention to preserving health data privacy. However, overly stringent protection measures can impede the efficient utilization of invaluable resources for medical research and personalized healthcare, posing a central challenge in balancing privacy protection with effective data utilization. This study aims to explore various methods used to protect the privacy of patients’ health records, and evaluates their advantages and limitations. Additionally, it conducts an in-depth analysis of a public medical dataset concerning privacy protection, assessing the effectiveness of k-anonymity and l-diversity privacy criteria and examining the influence of quasi-identifier (QID) attributes on privacy preservation. The study showcases techniques to achieve privacy standards, including generalization and suppression. Furthermore, it introduces a novel approach that utilizes the genetic algorithm (GA) and a non-dominated sorting technique to maximize both privacy and utility in health data through multi-objective optimization. After examining the results, this paper offers a guide for data owners on selecting attributes for medical data publication and choosing suitable privacy preservation strategies. Through the exploration of the GA and the non-dominated sorting approach, this paper suggests that the proposed GA can offer promising non-dominated solutions to the issue of health data privacy in the era of data-driven healthcare. A combination of these algorithms can enhance privacy protection and provide healthcare professionals and researchers with essential knowledge, ultimately benefiting patient care and ensuring a more secure database system.

In cloud computing, there have led to an increase in the capability to store and record personal data (microdata) in the cloud. In most cases, data providers have no/little control that has led to concern that the personal data may be beached. Microaggregation techniques seek to protect microdata in such a way that data can be published and mined without providing any private information that can be linked to specific individuals. An optimal microaggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the microaggregation process. This paper presents a sorting framework for Statistical Disclosure Control (SDC) to protect microdata in cloud computing. It consists of two stages. In the first stage, an algorithm sorts all records in a data set in a particular way to ensure that during microaggregation very dissimilar observations are never entered into the same cluster. In the second stage a microaggregation method is used to create k-anonymous clusters while minimizing the information loss. The performance of the proposed techniques is compared against the most recent microaggregation methods. Experimental results using benchmark datasets show that the proposed algorithms perform significantly better than existing associate techniques in the literature.

Microdata protection in statistical databases has recently become a major societal concern and has been intensively studied in recent years. Statistical Disclosure Control (SDC) is often applied to statistical databases before they are released for public use. Micro aggregation for SDC is a family of methods to protect micro data from individual identification. SDC seeks to protect micro data in such a way that can be published and mined without providing any private information that can be linked to specific individuals. Micro aggregation works by partitioning the micro data into groups of at least k records and then replacing the records in each group with the centroid of the group. An optimal micro aggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the micro aggregation process. This paper presents a pair wise systematic (P-S) micro aggregation method to minimize the information loss. The proposed technique simultaneously forms two distant groups at a time with the corresponding similar records together in a systematic way and then anonymized with the centroid of each group individually. The structure of P-S problem is defined and investigated and an algorithm of the proposed problem is developed. The performance of the P-S algorithm is compared against the most recent micro aggregation methods. Experimental results show that P-S algorithm incurs less than half information loss than the latest micro aggregation methods for all of the test situations.

Microdata protection in statistical databases has recently become a major societal concern. Micro aggregation for Statistical Disclosure Control (SDC) is a family of methods to protect microdata from individual identification. Micro aggregation works by partitioning the microdata into groups of at least k records and then replacing the records in each group with the centroid of the group. This paper presents a clustering-based micro aggregation method to minimize the information loss. The proposed technique adopts to group similar records together in a systematic way and then anonymized with the centroid of each group individually. The structure of systematic clustering problem is defined and investigated and an algorithm of the proposed problem is developed. Experimental results show that our method attains a reasonable dominance with respect to both information loss and execution time than the most popular heuristic algorithm called Maximum Distance to Average Vector (MDAV).