# 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.

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.

• 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 Feb 3, 2021 at 8:15

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