Model Extraction and Active Learning.

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, one being model extraction. 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. Such a user only sees the answers to select queries sent using the query interface. This attack was recently introduced by Tramer et al. at the 2016 USENIX Security Symposium, where practical attacks for different models were shown. We believe that better understanding the efficacy of model extraction attacks is paramount in designing better privacy-preserving MLaaS systems. To that end, we take the first step by (a) formalizing model extraction and proposing the first definition of extraction defense, and (b) drawing parallels between model extraction and the better investigated active learning framework. In particular, we show that recent advancements in the active learning domain can be used to implement both model extraction, and defenses against such attacks.