Anonymize continuous variable for masking purposes 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.

a logarithmic transformation is easily reverse engineered
a nonlinear transformation is better, but will mess with the relationship of values between categories
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.
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.