Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks

The wide adoption and application of Masked language models (MLMs) on sensitive data (from legal to medical) necessitates a thorough quantitative investigation into their privacy vulnerabilities – to what extent do MLMs leak information about their training data? Prior attempts at measuring leakage of MLMs via membership inference attacks have been inconclusive, implying the potential robustness of MLMs to privacy attacks. In this work, we posit that prior attempts were inconclusive because they based their attack solely on the MLM's model score. We devise a stronger membership inference attack based on likelihood ratio hypothesis testing that involves an additional reference MLM to more accurately quantify the privacy risks of memorization in MLMs. We show that masked language models are extremely susceptible to likelihood ratio membership inference attacks: Our empirical results, on models trained on medical notes, show that our attack improves the AUC of prior membership inference attacks from 0.66 to an alarmingly high 0.90 level, with a significant improvement in the low-error region: at 1 attack is 51X more powerful than prior work.


page 1

page 2

page 3

page 4


Membership Inference Attack Susceptibility of Clinical Language Models

Deep Neural Network (DNN) models have been shown to have high empirical ...

Revisiting Membership Inference Under Realistic Assumptions

Membership inference attacks on models trained using machine learning ha...

Gaussian Membership Inference Privacy

We propose a new privacy notion called f-Membership Inference Privacy (f...

Membership Inference Attacks against Language Models via Neighbourhood Comparison

Membership Inference attacks (MIAs) aim to predict whether a data sample...

Targeted Attack on GPT-Neo for the SATML Language Model Data Extraction Challenge

Previous work has shown that Large Language Models are susceptible to so...

On the Privacy Risks of Algorithmic Recourse

As predictive models are increasingly being employed to make consequenti...

Sparsity in neural networks can improve their privacy

This article measures how sparsity can make neural networks more robust ...

Please sign up or login with your details

Forgot password? Click here to reset