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Aggregate reporting from Turtledove #54
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Yes, all bidding functions should be able to send aggregate reports.
Yes, that seems right to me.
The privacy properties of aggregate reporting do need some sort of limit. For example, it might turn out that the more reports you send about the same auction, the more noise each one would be subject to, so that the total amount of information you reveal ends up staying the same. @csharrison FYI.
The Conversion Measurement API is the more developed of these proposals, and the Aggregate Reporting API's string-concatenation approach was only introduced to handle its unusual blind-append semantics. So I would expect numerical reporting here. But if there's some use case that you think can't be adequately supported with that limit, please do open an issue (probably on the Conversion Measurement API repo) and let's discuss it. |
Closing this issue as it represents past design discussion that predates more recent proposals. I believe some of this feedback was incorporated into the Private Aggregation proposal. If you feel further discussion is needed, please feel free to reopen this issue or file a new issue. |
Hi Michael,
In a couple places (#16, #35) it was mentioned that there will be a mechanism to gather aggregate data from Turtledove auctions. It seems that both Aggregate Reporting and Conversion Measurement with Aggregation proposals would be safe to use in Turtledove, but there is a number of questions that would be great to discuss:
These details impact what kinds of models we can design and train effectively, so it would be great if you could share your thoughts, even if you don't have an exact design in mind yet.
As a side note, there may be other interesting implications to these design choices. If we assume flexible reporting is available, Turtledove API may be used for the explicit purpose of gathering cross-site aggregate information for collaborating sites, for example "what percent of an advertiser's visitors also visit some chosen publisher". Another example would be the lookalike audience targeting use case (described in @benjaminsavage's proposal). Depending on the exact details (what data can we use for reports, whether numeric values are supported, ...) this use case could be supported to varying degrees.
Best regards,
Jonasz
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