iunie 24, 2020 —
Posted by Shuang Song and David Marn
Today, we’re excited to announce a new experimental module in TensorFlow Privacy (GitHub) that allows developers to assess the privacy properties of their classification models.
Privacy is an emerging topic in the Machine Learning community. There aren’t canonical guidelines to produce a private model. There is a growing body of research showing that a machine …
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Overview of a membership inference attack. An attacker tries to figure out whether certain examples were part of the training data. |
Today, we’re excited to announce a new experimental module in TensorFlow Privacy (GitHub) that allows developers to assess the privacy properties of their classification models.
Privacy is an emerging topic in the Machine Learning community. There aren’t canonical guidelines to produce a private model. There is a growing body of research showing that a machine learning model can leak sensitive information of the training dataset, thus creating a privacy risk for users in the training set.
Last year, we launched TensorFlow Privacy, enabling developers to train their models with differential privacy. Differential privacy adds noise to hide individual examples in the training dataset. However, this noise is designed for academic worst-case scenarios and can significantly affect model accuracy.
These challenges led us to tackle privacy from a different perspective. A few years ago, research around the privacy properties of machine learning models started to emerge. Cost-efficient “membership inference attacks” predict whether a specific piece of data was used during training. If an attacker is able to make a prediction with high accuracy, they will likely succeed in figuring out if a data piece was used in the training set. The biggest advantage of a membership inference attack is that it is easy to perform, i.e., does not require any re-training.
A test produces a vulnerability score that determines whether the model leaks information from the training set. We found that this vulnerability score often decreases with heuristics, such as early stopping or using DP-SGD for training.
iunie 24, 2020
—
Posted by Shuang Song and David Marn
Today, we’re excited to announce a new experimental module in TensorFlow Privacy (GitHub) that allows developers to assess the privacy properties of their classification models.
Privacy is an emerging topic in the Machine Learning community. There aren’t canonical guidelines to produce a private model. There is a growing body of research showing that a machine …