Fair enough, no there is no deep user interaction with the app it’s just a normal marketplace app, think Amazon app. I’ve just been relying on a bunch of research papers that seem to suggest that each of those data points individually yield unique profiles with high accuracy but I may be misunderstanding them… just a few:

- https://www.sciencedirect.com/science/article/pii/S1877050921015532

- https://www.sciencedirect.com/science/article/pii/S1877050918314996

- https://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=67C08602F99414F622E55151E2EC484C?doi=10.1.1.675.9557&rep=rep1&type=pdf

cool thanks!

I see, ok. This is encouraging to be honest, I knew there wasn't just going to be a magical solution that is easy to see but I think there is some research needed in this department. This is something that could be huge, and maybe it's not ML but just logic gates chained together.
You said any Neural Net would do? Any particular one you would recommend for testing?

I see, understood. You think harsh because it would be unreliable essentially? If it's possible is there no way of improving it or it will always be unreliable due to the nature of this method?

Right, I've been thinking about this for a bit and I'm not dead set on doing it like this but it seemed like there was a way so I wanted to explore. Unfortunately I'm not as smart as all you guys and gals but figured I'd ask for opinions.

I see, I like the black box aspect but I understand it makes things difficult for when we need consistent output... What kind of "key" would you be able to generate and with what models? What about mathematical or statistical ways to try to reduce the output to make it more stable? This might be a dum idea but imagine if the model spits out floats, we get 1.1 but we expect 1 we could apply rounding to get integers in which case we would more often get 1... or we could do multiple runs and average them out.. or use fancy math like finite fields, modulo arithmetic, using different base math, etc...
And yea I get it that we could use something that is on device but unfortunately that is not something I want to rely on.. nothing that is hard coded anywhere can be used.
The goal here is to generate this key and use it to encrypt/decrypt stuff. I never want to store this key anywhere, it needs to be generated by the user data fed into the model

I'm not quite sure I understand: "Some unique fingerprint has to come from some sort of behavioral or bio data that can reasonably be assumed to uniquely identify a user"
--> you mean to say "you have to get something unique from the user directly"? Because there are many ways to acquire unique things about a user.... how they type words into a keyboard is a very unique one for example, and there are many metrics that can be measured to figure that out...
- Pressure, Duration of press, Duration between presses, Speed
- Accuracy of presses
- use of backspace, use of auto-correct
- use of emojis, punctuation
- length of phrases, length of text, etc

Yea no worries, I can authenticate them differently at first and start tracking data for a while before it becomes important to have this key generated.

But this process you are describing is just to identify users individually using a standard test, not to generate a unique key per user... Is there some way I could achieve this? Generating a unique key from a machine learning model?

I see, I appreciate the advice. That being said, if the use case made sense for it and it is possible to do, how would you do it? Just as a thought experiment. If you had enough data and power

Right, yes it will require their consent but this information stays on device since the ML happens on-device as well. The full picture is that I'm trying to make a passwordless experience where the key generated by the ML model is their password and is used to encrypt and decrypt some data on the device as well (: Idk if that makes sense

Oh my bad, I've just re-written the post for more user-story type explanation...