[srean]

Could not get hold of the paper. Are they doing Gibbs sampling or a semiparametric variant of that ?

https://en.wikipedia.org/wiki/Gibbs_sampling

Generating tuples(row) by Gibbs sampling will allow generation of samples from the joint distribution. This in turn would preserve all correlations, conditional probabilities etc. This can be done by starting at a original tuple at random and then repeatedly mutating the tuple by overwriting one of its fields(columns). To overwrite, one selects another random tuple that 'matches' the current one at all positions other than the column selected for overwriting. The match might need to be relaxed from an exact match to a 'close' match.

If the conditional distribution for some conditioning event has very low entropy or the conditional entropy is low, one would need to fuzz the original to preserve privacy, but this will come at the expense of distorting the correlations and conditionals.

[malshe]

I could download it from here: https://dai.lids.mit.edu/wp-content/uploads/2018/03/SDV.pdf

Are you facing any trouble while accessing this link?

[srean]

Ah ! thanks it works.