Still sounds potentially problematic. Per wikipedia: "Differential privacy provides a quantified measure of privacy loss and an upper bound and allows curators to choose the explicit trade-off between privacy and accuracy. It is robust to still unknown privacy attacks. However, it encourages greater data sharing, which if done poorly, increases privacy risk. Differential privacy implies that privacy is protected, but this depends very much on the privacy loss parameter chosen and may instead lead to a false sense of security. Finally, though it is robust against unforeseen future privacy attacks, a countermeasure may be devised that we cannot predict."

If I am dependent on the curator to determine the level of privacy then I've already lost.

Differential privacy does not anonymize your data. It's a (mathematically solid) instrument to make it coarse enough to only give up the part you want.

The EFF is completely correct in their assessment - there's no way to make your data anonymous and usable by the third party at the same time. The root issue lies elsewhere.

There's FHE, which does allow your data to be processed without accessing it, but it has prohibitive compute requirements and isn't practical for most purposes. I'm not sure if it allows the data to be usable enough without revealing it, either.

Techniques like differential privacy do not work for some types of data models, including many of the more interesting/risky ones. I've never seen a technique that can deliver an analytical model at scale that is both analytically effective and anonymous while also robust against sophisticated de-anonymization attacks. There is no good theoretical foundation to suggest that such things are possible.

Most modern techniques for ensuring anonymization make assumptions that won't constrain sophisticated blackhats. They are good policy in a legal ass-covering sense and increase the cost required to de-anonymize but that is about it.

Or they don't want you collecting data in the first place, in which case the techniques are irrelevant.

In particular areas things like differential privacy may work, for example medical sets where there are lots of regulations and potential fines for the companies involved.

But do you think your average ad tech company gives a fuck? They are going to keep the original data because that's where the money is. Yea, maybe they'll have privacy datasets they sell/release to other groups, but all the real data will remain in a database, and with most companies in this industry, be given to government agencies on demand.

There's also Zero Knowledge Proofs which instead has a privacy guarantee

Edit: Not trying to start a DP vs ZKP (or Homomorphic Encryption) flame war. We're all on the same side of privacy, right? Different tools for different situations.