Using a 'three-layer data market' model (users, platforms, third-party data buyers), Jordan shows that offering users a tunable, priced level of differential privacy creates feedback loops that can be analyzed and optimized as an equilibrium problem, rather than left to chance. ✦ AI generated
Michael I. Jordan · Machine Learning Street Talk · 2026-05-20 · original ↗
starts at this moment · 34:36
“So um there was an example where you know you might have consumers and they might have their data and you've got Google and then you know Google is using the data the consumers are getting a service and then Google might sell the data over here. That's kind of like a traditional model. Let's start with that.”
In an effective economic system what would happen is that the platforms would say well we'll offer you a tunable level of differential privacy for some cost... the user looks at that and says, Ah, 7, that's better. I really care about my privacy, so I'll go there. That company then will get start to get more data and their service will get even better.
verbatim transcript · starts at 34:36
34:36something. They lost a little bit of privacy. Some third party that I don't know anything about is getting data about me. Um and I can't just accept that. Um you know, but I can't walk away, you know. So, there's a stress on the system now. So um in an effective economic system what would happen is that the uh you wouldn't just wait for the regulator to come in the government
34:56say you know no this can't be done what you would do is that the platforms would say well we'll offer you uh a tunable level of differential privacy for some cost or we'll just say that this our company I'm Google I'll offer you level you know.3 and some other company says well I'll offer you level 7. Okay. So, the user looks at that and says, "Ah, 7.
35:17That's that's better." Um, I I really care about my privacy, so I'll go there. That company then will get start to get more data and their service will get even better. And oop, you got a little nice little feedback loop there. But now the data buyers will look at the data from that person. At 7 means more noise has been added to the data. It's less valuable to the data buyer. Data buyer
35:38will say, "Oh, I'll I'll spend less. I'll give you less money for that. I'll give more money to Google." And so now you can see there's conflicting um tendencies here. The the incentives are aligned but they're not uh you're not optimal for everybody. Um and so now the mathematics is not just an optimization problem. The mathematics is an equilibrium problem. But it's an equilibrium problem that involves
36:01statistical uh assertions data and how much you can predict with this data and so on that. So you quantify that with error bars and and statistical predictions. So you put that all together in a big mathematical system and you can find the equilibria as a function of various system parameters. So for example, is there a minimal level of privacy the regulators could require or not or you know is there some
36:22heterogeneous privacy budget you know etc etc. You can put in various uh those and now you you do a little plot of how the equilibria moved and the equilibria have overall utilities for all the three players summed up. That's the social welfare. You can ask how high is the social welfare or that equilibrium versus this one versus this one. And another regular could look at that say