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the_Wallie t1_jdbwjfb wrote

it depends on what your users are doing in your app. Some unique fingerprint has to come from some sort of behavioral or bio data that can reasonably be assumed to uniquely identify a user. Encoding data in some meaningful way (ml or otherwise) can only happen after you choose what you're encoding.

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geoffroy_lesage OP t1_jdbwzjj wrote

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

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the_Wallie t1_jdd0t7w wrote

I don't think thst it's self-evident that all of those individual behaviors can actually yield a truly unique behavioral pattern per user for each type of app. Maybe when combined, if your app involves a lot of deep user interaction, but since you haven't shared what your app is supposed to actually do, it's impossible to give an informed opinion on your probability of success a priori, so all I can say is I'm skeptical but I wish you good luck building a solution.

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geoffroy_lesage OP t1_jde7chs wrote

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

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