Privacy in Practice: Private COVID-19 Detection in X-Ray Images

Abstract

Machine learning (ML) can help fight the COVID-19 pandemic by enabling rapid screening of large volumes of chest X-ray images. To perform such data analysis while maintaining patient privacy, we create ML models that satisfy Differential Privacy (DP). Previous works exploring private COVID-19 ML models are in part based on small or skewed datasets, are lacking in their privacy guarantees, and do not investigate practical privacy. In this work, we therefore suggest several improvements to address these open gaps. We account for inherent class imbalances in the data and evaluate the utility-privacy trade-off more extensively and over stricter privacy budgets than in previous work. Our evaluation is supported by empirically estimating practical privacy leakage through actual attacks. Based on theory, the introduced DP should help limit and mitigate information leakage threats posed by black-box Membership Inference Attacks (MIAs). Our practical privacy analysis is the first to test this hypothesis on the COVID-19 detection task. In addition, we also re-examine the evaluation on the MNIST database. Our results indicate that based on the task-dependent threat from MIAs, DP does not always improve practical privacy, which we show on the COVID-19 task. The results further suggest that with increasing DP guarantees, empirical privacy leakage reaches an early plateau and DP therefore appears to have a limited impact on MIA defense. Our findings identify possibilities for better utility-privacy trade-offs, and we thus believe that empirical attack-specific privacy estimation can play a vital role in tuning for practical privacy.