Stakeholders can use precision and recall metrics to evaluate the effectiveness and potential risks of membership attacks. Precision measures the accuracy of positive predictions made by an attack, indicating how likely a prediction is correct. On the other hand, recall measures the fraction of individuals for which predictions can be made accurately. By considering both precision and recall values, stakeholders can assess the overall performance of an attack in terms of its ability to correctly identify members from non-members.
In assessing risk, stakeholders should look for high precision values as they indicate that when a prediction is made by an attack, it is more likely to be correct. High recall values show that a larger proportion of individuals are correctly identified as members or non-members. A balance between precision and recall is crucial; high precision with low recall may suggest that while predictions are accurate, many potential members might be missed.
By analyzing precision-recall curves derived from ROC curves on log-log scales or through direct computation using observational skews, stakeholders can gain insights into how well an attack performs across different levels of observational skewness. This detailed analysis allows them to understand the trade-offs between making accurate predictions (precision) and capturing all relevant instances (recall), ultimately helping in evaluating the actual risk posed by membership attacks.