Differential privacy (and data privacy, more generally) is commonly mistaken as a sub-topic of security or cryptography, but the two fields have different purposes. With security, you are limiting access to data to authorised people only (i.e. someone with a key or password to access the data). With differential privacy and data privacy, you are looking to publish (statistics of) sensitive data to the public without revealing personal information about the individuals in the dataset. The mathematical guarantees of differential privacy ensures that you can't undo the noise that has been added in the process, ensuring that the privacy protocol can't be "de-crypted".

The broad goal of differential privacy is to preserve the privacy of individuals while allowing population statistics to be accurately observable. So in the context of adding noise to ages, you own age is obfuscated (to prevent things like linkage attacks), but population statistics can still be computed accurately (i.e., the average age of people in the dataset). This concept extends to much more complex "queries", like training a machine learning model.

It's true, the only way to fully preserve privacy is not to release data, but differential privacy is the best compromise we have. Having a way to extract knowledge from data with mathematical guarantees on individuals' privacy is important to have. The census is a good example, because the Census is required by law to collect data, and making it available delivers various benefits to the population.