> Okay, so the platform becomes valuable to its users when it's able to suggest things like "based on millions of users, people with cycles like yours typically ovulate around day 16."

I don't know much about period tracking apps, but is this really the main reason people install those apps? Wouldn't you be able to get similar results by simply monitoring (on-device) the cycle of the person who uses the app for a few months?

How do those apps work before they have millions of users?

All the warnings I've seen about period tracking apps were about unexpected data collection of the entered data. This would be pretty silly if the data collection was integral to what the user expects the app to do.

> Sure, you could just do it locally, but then you miss out on the aggregate data mining.

Ok, a bit of a technical question about FHE here: My understanding of FHE was that you have input data encrypted with some key (plus auxiliary inputs, if needed, that are not encrypted), then you do operations on that data and get a result that is (still) encrypted by that same key.

No questions there as long as you're dealing with a single key.

But the whole point of aggregation and data mining is to combine data from many different users, i.e. inputs that are encrypted by many different keys. Does that work with FHE at all? And if yes, by which key is the aggregation result encrypted?

I don't see how that would work without either "moving" data from one key to another - which would be practically equivalent to decryption - or getting a result that is simultaneously encrypted by all user keys, i.e. practically useless because no one could individually decrypt it.

> The compelling product claim is "we literally cannot read your period data." Not "we pinky swear not to" but "we actually really really actually can't!"

You could obviously read the data enough to do aggregations on it.

If you can do that for "good" purposes, what stops you to use the same aggregation algorithm for advertisers - except pinky promises again?