Dissecting Distribution Inference

(Cross-post by Anshuman Suri) Distribution inference attacks aims to infer statistical properties of data used to train machine learning models. These attacks are sometimes surprisingly potent, as we demonstrated in previous work. KL Divergence Attack Most attacks against distribution inference involve training a meta-classifier, either using model parameters in white-box settings (Ganju et al., Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations, CCS 2018), or using model predictions in black-box scenarios (Zhang et al.

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Cray Distinguished Speaker: On Leaky Models and Unintended Inferences

Here’s the slides from my Cray Distinguished Speaker talk on On Leaky Models and Unintended Inferences: [PDF] The chatGPT limerick version of my talk abstract is much better than mine: A machine learning model, oh so grand With data sets that it held in its hand It performed quite well But secrets to tell And an adversary’s tricks it could not withstand. Thanks to Stephen McCamant and Kangjie Lu for hosting my visit, and everyone at University of Minnesota.

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On the Risks of Distribution Inference

(Cross-post by Anshuman Suri) Inference attacks seek to infer sensitive information about the training process of a revealed machine-learned model, most often about the training data. Standard inference attacks (which we call “dataset inference attacks”) aim to learn something about a particular record that may have been in that training data. For example, in a membership inference attack (Reza Shokri et al., Membership Inference Attacks Against Machine Learning Models, IEEE S&P 2017), the adversary aims to infer whether or not a particular record was included in the training data.

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