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.