10MA – Security and Machine Learning

When training ML models, there can be some security aspects which are important. Here are some examples:

Some security goals.

  1. Training set privacy. An adversary which is familiar with the model, can not get “any” information on the data-points in the training set.
  2. Model secrecy. An adversary able to get predictions for any input by the model as a black-box, can not obtain information about the model parameters.
  3. Model reliability. The model should behave in a way that humans can predict.

Links to related attacks

  1. Membership Inference Attacks against Machine Learning Models
  2. Stealing Machine Learning Models via Prediction APIs
  3. Breaking Linear Classifiers on ImageNet
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