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Understanding Aggregate Trends for Apple Intelligence Using Differential Privacy

(machinelearning.apple.com)
52 points layer8 | 1 comments | 14 Apr 25 20:04 UTC | HN request time: 0.205s | source
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mattnewton ◴[15 Apr 25 02:27 UTC] No.43688466[source]▶
I worked on a similar system at Google for gboard, the Google branded android keyboard that we called “federated analytics” - it worked with device-to-device communication and invertable bloom lookup tables. I’m still not super sure how the Apple system works after reading it, but I don’t see ant mention of using data structures like that, instead they are polling the devices themselves it seems? Does anyone else have more insight to the mechanics, because that seems super inefficient?

https://research.google/blog/improving-gboard-language-model...

replies(1): >>43695546 #
1. matthewdgreen ◴[15 Apr 25 16:59 UTC] No.43695546[source]▶
I went looking for exactly this information the other day. I was surprised to find that it's hard to come up with recent, detailed explanations of what Apple is doing for telemetry collection. When they announced their DP systems back in 2017, they were clearly doing something like Google's RAPPOR [1]. But it's been several years since then and their writeups haven't been updated very much at all [2].

This is pretty important, because these systems aren't so robust that you can just assume everything is working without review. (See, for example, this paper [3].) Apple should at least document what kinds of data are being collected, and precisely how the collection process works.

[1] https://static.googleusercontent.com/media/research.google.c... [2] https://www.apple.com/privacy/docs/Differential_Privacy_Over... [3] https://arxiv.org/pdf/1709.02753