Cited on HN 4 times, 2017–2018, never outright recommendedscore 0
Years with an HN recommendation, 2007–2026
Cited in threads about Google's purchase tracking as the technical source for how the matching might work.
A Real World Crypto 2017 talk by a Google engineer on using private set intersection to match ad views with in-store purchases. It describes privacy-preserving matching that avoids exposing personal transactions.
“According to what they presented at a crypto conference, they went to great pains to avoid capturing personally identifiable transactions.” (HN, 2018)
A Googler gave a talk at Real World Crypto in January describing a system that might back this. The relevant part of the talk starts around the 10 minute mark: [this video] In short, they're able to compute the intersection of the set of users who have viewed an ad with the set of people who purchased a product in a store, without either party disclosing their side of the set.
Google talk on private set intersection for ad measurement
It's actually not given to Google in a user identifiable way. I am not privy to the details, but an overview was presented at a crypto conference: [this video] It looks to me like a form of homomorphic encryption where both the aggregate consumer transactions (people bought X at merchant M), and the aggregate Google ad information (people saw ad for X at merchant M) are encrypted and the set intersection is computed on the ciphertext revealing only the aggregate number of purchases, not the individuals. Now granted, there could be a security hole in this scheme as with any crypto-protocol, so it does need peer review, but the design, as stated in that YouTube presentation, is not to reveal to Google personal purchasing information, nor reveal to merchants, personal profile information. No personal financial data or purchasing information is supposedly transmitted. The problem with this kind of technology is that it is really difficult to explain to the public, and easily demagogued.
IIRC, this was announced as part of a program that uses double-blinded crypto that allows google to perform matching against this data in aggregate (Set intersection knowledge), but not individually identity of what a given user purchased, at the same time preventing the merchants from learning the identities of individuals either. According to what they presented at a crypto conference, they went to great pains to avoid capturing personally identifiable transactions. The overall strategy is that Google Knows = Set A = (all ads shown for user X at Merchant Y) Merchant/Credit Card Proccessor Knows = Set B = (all $$$ paid by User X at Merchant Y), and that it is possible to cryptographically compute sum($$$ of A intersect B) without either side learning Set(User X). Now whether you believe in the security of the algorithm and whether there are side channel leaks or attacks is another issue. Edit: [link] About 10 minutes in [this video]
Source for Google's double-blinded set intersection approach
> Google buys your credit card purchase data. A more accurate explanation of the scare quote: [link] > IIRC, this was announced as part of a program that uses double-blinded crypto that allows google to perform matching against this data in aggregate (Set intersection knowledge), but not individually identity of what a given user purchased, at the same time preventing the merchants from learning the identities of individuals either. > According to what they presented at a crypto conference, they went to great pains to avoid capturing personally identifiable transactions. The overall strategy is that Google Knows = Set A = (all ads shown for user X at Merchant Y) Merchant/Credit Card Proccessor Knows = Set B = (all $$$ paid by User X at Merchant Y), and that it is possible to cryptographically compute sum($$$ of A intersect B) without either side learning Set(User X). > Now whether you believe in the security of the algorithm and whether there are side channel leaks or attacks is another issue. > Edit: [link] > About 10 minutes in [this video]
Quoted citation of Google's privacy-preserving matching
Video list and Hacker News comment labels from HackerTube (prompted by @karpathy), released under ODC-By 1.0, built on the open-index/hacker-news archive. Hacker News data is provided by Y Combinator; comments remain the work of their authors. Video titles, channel names and thumbnails belong to their creators and are shown via YouTube. HackerTube Watch is an unofficial edition. Data export of 2026-10-31.
4 Hacker News comments
From the HN archive; up to 20 kept per video, favoring different commenters. Recommendations first.
Read more
Read more
Read more