- From: benjaminsavage via GitHub <sysbot+gh@w3.org>
- Date: Thu, 23 Jun 2022 05:51:34 +0000
- To: public-patcg@w3.org
> I have strong concerns about enforcing k = 100 , since for some advertisers conversions can be quite rare events and even a relatively tight epsilon should give good data for many values of k < 100 (e.g. eps=1 will yield only a ~15% error on counts of 10). Two thoughts: Firstly, I agree that conversions are rare. The **vast majority** of Facebook advertisers have only a handful of conversions to measure each week. I care a great deal about supporting small businesses and I want to develop an API that can support their measurement needs. But just to be clear, I am proposing K=100 applies to the **impressions**, not to the **conversions**. Even the smallest advertisers who just spend a few dollars wind up getting at least 100 impressions. Just to give an example, if an advertiser spent $5 on ads and got 200 impression, which led to just 3 conversions, that would pass this proposed bar. We would need to add some random noise to the number 3, so the API might add some Gaussian noise, but so long as those 3 conversions originated from a group of > 100 people we would pass this bar. The thinking here is that we can say: "Yeah, there were roughly 3 conversions. We do not know which of these 100 people they came from." This feels like a pretty simple to communicate privacy story. Blending in with a crowd of 100+ people is something all of us have experience with every day. -- GitHub Notification of comment by benjaminsavage Please view or discuss this issue at https://github.com/patcg/private-measurement/issues/17#issuecomment-1163978878 using your GitHub account -- Sent via github-notify-ml as configured in https://github.com/w3c/github-notify-ml-config
Received on Thursday, 23 June 2022 05:51:36 UTC