ABSTRACT
The COVID-19 pandemic has led to an increase in digitization. With the strict social and physical distancing measures in place, new routines require accessing the internet for most online services which have led to the explosive growth of data. As a consequence, data mining technologies are used for the extraction of useful information from a huge compilation of such digital data. Thus, the desire to mine data from varied sources to discover behaviors and patterns among entities such as customers, diseases, and environmental conditions is on the rise which can be accomplished by association rule mining. However, such pattern discovery by association rule mining also discloses the personal information of an individual or organization. Thus, the challenge of association rule mining is privacy preservation wherein confidentiality of sensitive rules should be maintained while releasing the database of third parties. Privacy-preserving association rule mining is the process of modifying the original database to hide the sensitive rules for preserving privacy. Thus, the paper emphasizes multiple objectives like minimizing the side effects of hiding sensitive rules. i.e. reduce the number of ghost rules, lost rules, and hiding failure along with the increase in utility of the data. Copyright © 2022 by the authors.