Due to the rapid evolution of data saved in electronic form, data mining technologies have become critical and indispensable in looking for nontrivial, implicit, hidden, and possibly beneficial information in enormous volumes of data. High Utility Pattern Mining (HUPM), among the most intriguing data mining techniques, is broadly leveraged to analyze business interactions in market data based on the notion of economic utilities. These economic utilities can be used to examine the factors influencing a customer's purchasing behavior or to come up with new tailored selling and promotion tactics. This in turn has made utility-driven techniques an essential operation and vital activity for many data analysts since they can lead to proper decision-making processes. Nevertheless, such techniques can also lead to major threats regarding privacy and information security if they were misused. Privacy-Preserving Utility Mining (PPUM), also known as High Utility Pattern Hiding (HUPH), has recently emerged to mitigate the security and privacy issues that could happen in the utility framework. In this paper, we propose a heuristic PPUM method, named HUP-Hiding, to protect the results when mining sensitive data using a utility mining algorithm. The proposed method employs a dataset projection mechanism and a new victim item selection technique to efficiently perform the sanitization process. Experiments were performed to verify the reliability of the suggested algorithm. Our experimental results on different datasets confirm that HUP-Hiding has reasonable performance and fewer side effects compared to existing approaches.