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I am about to kick off a large hackathon event.

We have a dataset that is comprised of one continuous variable with high precision, and a number of categorical variables qualifying these data 3-levels deep.

Data provider wants to 'mask' the data such that the original values cannot be reverse-engineered. I'm not worried about the categorical variables, this is simple. But the continuous variables are tricky.

  1. a logarithmic transformation is easily reverse engineered
  2. a nonlinear transformation is better, but will mess with the relationship of values between categories
  3. a pure linear transformation would work, but doesn't seem to 'mask' enough.

I need to preserve the relationships between numbers whilst also protecting the actual, true values.

Ideas greatly appreciated.

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  • $\begingroup$ theoreticaly any one-one transformation is invertible (no matter linear or not), on the other hand a not one-one transformation messes up the problem $\endgroup$
    – Nikos M.
    Feb 3, 2021 at 8:15

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I think you can use a much more complicated monotonic transformation, like

log(1.234578 + sqrt(x + 7.4142) ** 3)

which will be harder to invert than a simple log. But, as Nikos says, strictly monotonic functions are invertible, so all you can do is make it very hard to compute the inverse by composing many monotonic functions.

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  • $\begingroup$ maybe use some "unique" hash (cryptographic) function with unique key/salt? $\endgroup$
    – Nikos M.
    Feb 3, 2021 at 16:24
  • $\begingroup$ is it possible not to lose the monotonicity? can you provide an example? $\endgroup$
    – David Masip
    Feb 3, 2021 at 16:43
  • $\begingroup$ I like this approach, but doesn't this transform, while monotonic, screw with the relationships between numbers? e.g., a regression run on the transformed numbers won't yield the same parameter estimates as the original numbers, right? $\endgroup$
    – HEITZ
    Feb 3, 2021 at 16:52
  • $\begingroup$ yeah, the parameters will change, but they change when you apply a logarithm too. However, given a model you'll always be able to recover the model on the original variables by calculating the inverse of the transformation function $\endgroup$
    – David Masip
    Feb 3, 2021 at 16:57
  • $\begingroup$ I implied using a "one-one" cryptographic hash (with unique key/salt) as the desired transform, but.. $\endgroup$
    – Nikos M.
    Feb 3, 2021 at 17:50

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