dp_throw

differential privacy is good for answering population questions like "how many people in my dataset have property x?". it's a lot less clear how to apply it to something as granular as serving personalized ads. and as the example demonstrates, this compounds if you're doing it repeatedly with data that keeps getting updated. to the best of my knowledge, "differentially private personalized ads" is a hard problem, and maybe just a contradiction in terms.

Just to add to the nice responses from lomereiter and georgefox, I think the common response to

> For official statistics and scientific research this is often not an acceptable tradeoff

is that differential privacy is the best known method for rigorously accounting for privacy risks. It's possible to argue that differential privacy is too strong (and plenty of people have), but to the best of my knowledge, systems that say "you don't need DP - we'll answer lots of database queries without DP and still prevent deanonymization" usually end up getting broken. A good example of this is the (repeated) breaking of Diffix [1], a system that attempts to provide privacy without using differential privacy.

So differential privacy is, I think, a good starting point if privacy is critical to your application. It does not offer much guidance for when you should decide privacy is critical, or when the utility of an application outweighs the need for privacy.

For example, many social science researchers have criticized the US Census for using differential privacy in the 2020 census. It's consistent to say "it's way more important to have accurate counts for all of the decisions made using census data -- let's not try too hard to be private". It's also consistent to say "privacy is important, so we should use a rigorous notion like differential privacy". It's not consistent to say "private is important, but let's just use some heuristics and hope for the best", which is what the census had largely been doing until 2020.

[1] https://differentialprivacy.org/diffix-attack/

The process of only releasing information that describes groups of people is more broadly known as k-anonymity [1]. This comment does a pretty good job of describing its appeal (it makes some intuitive sense, and it's better than doing nothing) and drawbacks (it's vulnerable to side information, releasing even two k-anonymous outputs can reveal a lot of information, and it can get pretty complicated).

Differential privacy solves these problems because it gives mathematical bounds for the amount of privacy lost. If you take part in two 0.5-differentially private computations, the probability of any event becomes at most e times more or less probable as a result of those computations. Of course, differential privacy has a lot of work left to do before it's feasible to just insert it into general data analysis, but it's slowly moving along that road.

[1] https://en.wikipedia.org/wiki/K-anonymity