  Machine learning is being increasingly used by individuals, research
institutions, and corporations. This has resulted in the surge of Machine
Learning-as-a-Service (MLaaS) - cloud services that provide (a) tools and
resources to learn the model, and (b) a user-friendly query interface to access
the model. However, such MLaaS systems raise privacy concerns such as model
extraction. In model extraction attacks, adversaries maliciously exploit the
query interface to steal the model. More precisely, in a model extraction
attack, a good approximation of a sensitive or proprietary model held by the
server is extracted (i.e. learned) by a dishonest user who interacts with the
server only via the query interface. This attack was introduced by Tramer et
al. at the 2016 USENIX Security Symposium, where practical attacks for various
models were shown. We believe that better understanding the efficacy of model
extraction attacks is paramount to designing secure MLaaS systems. To that end,
we take the first step by (a) formalizing model extraction and discussing
possible defense strategies, and (b) drawing parallels between model extraction
and established area of active learning. In particular, we show that recent
advancements in the active learning domain can be used to implement powerful
model extraction attacks, and investigate possible defense strategies.
