# Wanting to overfit model to say if data has been seen before

I would like to train a scalable model that has as an input row of a database and has an output of either 1 or 0 depending upon whether it has seen this entry of the database before or not. The purpose of this is to then be able to check against a similar database and determine which entries are the same in the first database, without ever having to share the database. I can't hash the entries and hash-match or any other techniques like that.

That is, let's say two organizations are both using a person database with just floats and with columns = ["name" : float, "age": float, "net_worth": float]. Now, imagine that I am the first group, and I train a model that will overfit on my data, 'memorizing' it in some way. Then, what I want to be able to do is send that model to the other organization, who could apply it to each element of their table. This will then tell the other organization which entries of their dataset are also shared by me.

I understand that this is a unique (if not downright weird) way to try and solve this problem, but this is the approach that I'm trying to take. Does anyone have any solutions they can think of?

• Technically by sending the model to the other organization you would be sending them a lot of information about the first database. Hashing the entries seems a much safer way, why you can't use it? – Erwan Sep 16 '19 at 13:50
• Legal requirements about data sharing in Australia, it's a workaround – Milan Leonard Sep 18 '19 at 2:23
• Are you sure this is a legal workaround then? A model may contain a lot of personal information, usually readable in clear. A "maximally overfit" model would be exactly the original training data itself. – Erwan Sep 18 '19 at 8:15