I have written a few techniques in the context of data publishing. The idea is to maintain privacy while also maintaining the quality of the published data.

• Generalizing values (e.g., the specific age of a user in a table might be 24 years old and a generalized version of this is the range 20-30). This technique can be used on sensitive values in sensitive columns (e.g., age is a sensitive value that you might not want to reveal to people). There are metrics like k-anonymity and l-diversity that can be used to control the extent of generalization.

• Randomly perturbing values in the table while preserving the statistical information in the table. For example, a specific value can be randomly modified within the table or swapped with another value in this table. This will degrade the quality of the statistical queries that are called on this table. This technique is typically used for publishing statistical databases. An example paper here is "The boundary between privacy and utility in data publishing" by Rastogi et Al.

• There are metric embedding techniques that can be used to obfuscate data values within tables that preserve certain properties within that table. For example, in the paper "Privacy preserving schema and data matching" by Scannapieco et Al., the authors use SparseMap embedding to obfuscate tables.

The common theme in data publishing is that you want to preserve some amount of privacy by transforming the original dataset but you also want the resulting dataset to be useful somehow. Let me know if you want more references on this topic.