Data privacy is of paramount importance in today's digital age, with a growing need to protect sensitive information while still enabling data analysis. In the era of data-driven decision-making, ensuring data privacy has become a paramount concern. In order to protect sensitive information while still making data usable for analysis, association rule concealing algorithms have emerged as a potential approach. Hiding algorithms, particularly that using association rule mining, have developed as powerful methods for striking this equilibrium. In this study, we show how FP-growth stacks up against Apriori in terms of performance. Results are broken down by execution time, instance count, and trust in the Supermarket data set to draw conclusions about performance. Both the algorithms and their experimental results are provided. Compared to the Apriori approach, the FP-growth technique is roughly an order of magnitude quicker and more scalable, as shown by our performance analysis.

Association rule, Hiding, Data mining, Algorithm, Growth