title: Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
abstract: Some machine learning applications involve training data that is sensitive, such
  as the medical histories of patients in a clinical trial. A model may
  inadvertently and implicitly store some of its training data; careful analysis
  of the model may therefore reveal sensitive information.
  
  To address this problem, we demonstrate a generally applicable approach to
  providing strong privacy guarantees for training data: Private Aggregation of Teacher Ensembles (PATE). The approach combines, in
  a black-box fashion, multiple models trained with disjoint datasets, such as
  records from different subsets of users. Because they rely directly on sensitive
  data, these models are not published, but instead used as ''teachers'' for a ''student'' model. 
  The student learns to predict an output chosen by noisy voting
  among all of the teachers, and cannot directly access an individual teacher or
  the underlying data or parameters. The student's privacy properties can be
  understood both intuitively (since no single teacher and thus no single dataset
  dictates the student's training) and formally, in terms of differential privacy.
   These properties hold even if an adversary can not only query the student but
  also inspect its internal workings.
  
  Compared with previous work, the approach imposes only weak assumptions on how
  teachers are trained: it applies to any model, including non-convex models like
  DNNs. We achieve state-of-the-art privacy/utility trade-offs on MNIST and SVHN
  thanks to an improved privacy analysis and semi-supervised learning.
  
authors: Nicolas Papernot Martín Abadi Úlfar Erlingsson Ian Goodfellow Kunal Talwar
hIndex: Ian Goodfellow 64