Re: [private-measurement] Interoperable Private Attribution (IPA) (#9)

> I agree. I would really like for IPA to support queries where the source_events span multiple source sites. I think this is a key use-case for ad-networks that show ads across the open web. We discuss this possible extension in our IPA proposal in the "[business privacy grain](https://docs.google.com/document/d/1KpdSKD8-Rn0bWPTu4UtK54ks0yv2j22pA5SrAD9av4s/edit#heading=h.3fuxnvaozwm4)" section. It's really hard though, and we haven't yet worked through all the issues with this. In particular, it requires careful design to ensure a malicious helper node cannot violate the "Vegas Rule".

I don't think business privacy grain is necessary here. The thread there is about combining reports across publishers e.g. for a given advertiser. My understanding is that IPA supports this by default (and uses, in that example, the advertiser privacy unit).

> Reading through that thread, the use-case is really about training ML, not reporting. Rather than trying to get hundreds of independent breakdowns out of the API, it would probably be more efficient (from a DP perspective) to just train an ML model in MPC, and emit a trained model (with DP noise added). We allude to this as a possible future extension: [link](https://docs.google.com/document/d/1KpdSKD8-Rn0bWPTu4UtK54ks0yv2j22pA5SrAD9av4s/edit#heading=h.82taoxx5dmqm). This would have the added benefit of being able to model the interaction effects between these features.

Yes, I used this mostly as an example, to understand the limitations of IPA. Obviously if we can train models directly in IPA it will probably be more efficient, but supporting the Criteo competition setting is a decent litmus test on how powerful the reporting use-case is. As far as I understand, supporting a setting like this could allow us to do logistic regression in a pretty privacy-efficient way.

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Received on Monday, 9 May 2022 17:30:20 UTC