How can someone change the cross validation or holdout procedures in mlr so that before testing with the validation set, that same validation set is changed according to a procedure, namely the reusable holdout procedure?
Procedure: http://insilico.utulsa.edu/wp-content/uploads/2016/10/Dwork_2015_Science.pdf
Short answer: mlr doesn't support that.
Long answer: My experience with differential privacy for machine learning is that in practice it doesn't work as well as advertised. In particular, to apply thresholdout you need a) copious amounts of data and b) the a priori probability that a given classifier will overfit on the given data -- something you can't easily determine in practice. While the paper you reference comes with example code that shows that thresholdout works in this particular case, but the amount of noise added in the code looks like it was determined on an ad-hoc basis; the relationship to the thresholdout algorithm described in the paper isn't clear.
Before differential privacy can be robustly applied in practice in scenarios like that, mlr won't support it.