Data minimization, a core principle in data protection regulations, is often hailed as a mechanism for privacy protection. However, recent research has shown that data minimization alone does not guarantee privacy. This is due to the inherent correlations between various features in real-world data. Minimizing data might still allow for confident reconstruction of sensitive information, potentially leading to privacy violations. This research emphasizes the need for a more nuanced approach to privacy protection. It suggests that while data minimization plays a role, it is not a sufficient measure. Organizations must implement comprehensive privacy-preserving techniques, such as differential privacy and homomorphic encryption, to effectively safeguard sensitive information.