Simply classify data into buckets for each value of each variable such that each unique combination of buckets for every variable contains at least N items for a suitably large number of N. It is then mathematically impossible to match an individual data point to any set of original data points less than N.
I know this is a bit of an absurd example, but I'm trying to understand the mechanics more than to provide a realistic counterexample.
What are the variables and what is the value of N when you're talking about "freeform text contracts containing PII"?
That's not what this data is and that's broadly not what LLMs are trained on.
and obviously partial information is very useful for exactly those with some other partial information.
this is why OSINT is very effective. it's possible to piece together enough constraints on these datasets that in the end the likelihood of a successful deanonymization is unexpectedly high.