I think it should be noted that there was a lot of dissatisfaction from users of the census data as far as I know. So it's not been banned just for politicals sake or because they hate privacy... Some people I talked to in the privacy field even called the whole thing a total disaster and weren't shy to put blame on John Abowd who apparently pushed this through despite a lot of internal opposition and concerns. Not sure if that's true, but what is definitely true is that the way the data was released produced serious issues downstream as most researchers and statisticians that ingested the data weren't prepared for receiving noisy data values. Differential privacy was applied in a way such that many invariants that data users cared about weren't preserved, which was expected as it's not possible as you can't preserve all invariants and at the same time add meaningful noise to the data. The thing is, with such a differentially private data release you need to adapt all of the downstream analyses to take into account the exact mechanism the data was altered in. And since the census bureau used a very intricate mechanism that didn't just add Laplace noise to data values but instead relied on a multi-stage process that preserved some invariants but not others it was very difficult to even write routines to account for the changes being made to the data. They essentially asked of every data user to rewrite their whole analysis pipeline based on the exact disclosure mechanism that contained a large number of bespoke choices regarding which data invariants to preserve and basically produced a mix of noisy, synthesized data that was just really hard to reason about. I don't even know if there even would've been a way to do this better, but the fact is that not every small county or school district has top-tier statisticians at hand that can just read a whole monograph on differentially private synthesized census data and then hotpatch their existing analysis systems to work with that data.

I was a big fan of differential privacy but now I think it might be doing more harm than good, as I haven't seen a single case where it was applied successfully in a problem where it actually mattered, and it contributed strongly to discrediting and preventing a lot of work on other anonymization techniques as it was deemed the only way to preserve privacy by the research community, so showing up with enhancements to k-anonymity or any other noise mechanism not rooted in it was a sure way to get ridiculed and ignored. And it's just not a practical mechanism, even when it works for a single disclosure you always end up having to blow up the privacy budget to a ridiculous amount in order to keep disclosing statistics as otherwise you would for almost all real-world data run out of budget after a few publications.

So, for me it's a technique that works in the areas where it doesn't really matter (publishing highly aggregated statistics that pose almost zero privacy risk even without differential privacy) and doesn't work in other areas where it would actually matter (publishing fine-grained data about individuals or small groups). There are some niche use cases but in my view the privacy community has really overblown the importance of differential privacy by portraying it as the only way to reliably anonymize data.

BTW the German census bureau has an interesting approach to anonymization which they use for several decades already and so far I haven't heard of any cases of successful de-anonymization of the data, maybe the US bureau should have a look at that for their own needs.