  Model extraction attacks are a kind of attacks where an adversary obtains a
machine learning model whose performance is comparable with one of the victim
model through queries and their results. This paper presents a novel model
extraction attack, named TEMPEST, applicable on tabular data under a practical
data-free setting. Whereas model extraction is more challenging on tabular data
due to normalization, TEMPEST no longer needs initial samples that previous
attacks require; instead, it makes use of publicly available statistics to
generate query samples. Experiments show that our attack can achieve the same
level of performance as the previous attacks. Moreover, we identify that the
use of mean and variance as statistics for query generation and the use of the
same normalization process as the victim model can improve the performance of
our attack. We also discuss a possibility whereby TEMPEST is executed in the
real world through an experiment with a medical diagnosis dataset. We plan to
release the source code for reproducibility and a reference to subsequent
works.
