Differential privacy should be used when you need strong mathematical guarantees that individual data points cannot be re-identified, even when combined with other datasets, whereas traditional anonymization techniques may not provide the same level of protection against re-identification attacks. Differential privacy is particularly suitable for scenarios where data is being shared or analyzed in aggregate, and there is a need to balance data utility with privacy.

---