De-identification, the process of removing or masking identifiable information from a dataset, is insufficient for ensuring privacy because it can be vulnerable to re-identification. This vulnerability arises when de-identified data is combined with other information sources, potentially revealing individual identities. These limitations exposed the need for more secure methods like differential privacy, which provide stronger guarantees against re-identification. Differential privacy adds mathematical rigour and controlled randomness to data analysis, ensuring that individual privacy is preserved even when data is shared or used for research purposes.