T-closeness adds more requirement on top of L diversity.
L diversity focus on ensuring that sensitive attributes are different. However one may be able to get sensitive info.
T closeness solve this problem by adding additional constraint that the distribution of sensitive attributes within each equivalence class is close to the distribution of the sensitive attributes in the overall dataset.
This ensures that the sensitive attributes are diverse, and that the distribution of sensitive attributes is not skewed.