Enhancing Social Media Privacy With Apriori

ABSTRACT The rapid growth of social media platforms has intensified the challenge of managing user privacy in a manner that aligns with individual preferences. This study utilizes the Apriori algorithm to analyze user privacy settings across various social media applications, aiming to uncover patterns and associations that inform personalized privacy management solutions. By applying the algorithm to data collected from an online survey of 676 participants, we identified 611 unique privacy configuration patterns, revealing diverse user preferences. Key findings indicate significant associations between enabled and disabled settings, highlighting user tendencies in managing privacy across platforms. This approach enhances our understanding of privacy behavior and underscores the inadequacy of one‐size‐fits‐all privacy settings. The insights generated pave the way for the development of more adaptive, user‐centric privacy controls that adhere to legal and ethical standards. Future work should focus on employing these findings to promote innovative machine learning integrations with association rule mining, ultimately advancing the effectiveness of digital privacy solutions in computational analysis and information security.