Quantifying (Hyper) Parameter Leakage in Machine Learning. (arXiv:1910.14409v1 [cs.CR])

Black Box Machine Learning models leak information about the proprietary
model parameters and architecture, both through side channels and output
predictions. An adversary can thus, exploit this leakage to reconstruct a
substitute architecture similar to the target model, violating the model
privacy and Intellectual Property. However, all such attacks, infer a subset of
the target model attributes and identifying the rest of the architecture and
parameters (optimally) is a search problem. Extracting the exact target model
is not possible owing to the uncertainty in the inference attack outputs and
stochastic nature of the training process.

In this work, we propose a probabilistic framework, Airavata, to estimate the
leakage in such model extraction attacks. Specifically, we use Bayesian
Networks to capture the uncertainty, under the subjective notion of
probability, in estimating the target model attributes using various model
extraction attacks. We experimentally validate the model under different
adversary assumptions commonly adopted by various model extraction attacks to
reason about the attack efficacy. Further, this provides a practical approach
of inferring actionable knowledge about extracting black box models and
identify the best combination of attacks which maximise the knowledge extracted
(information leaked) from the target model.

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