  Previous studies have developed fairness methods for biased models that
exhibit discriminatory behaviors towards specific subgroups. While these models
have shown promise in achieving fair predictions, recent research has
identified their potential vulnerability to score-based membership inference
attacks (MIAs). In these attacks, adversaries can infer whether a particular
data sample was used during training by analyzing the model's prediction
scores. However, our investigations reveal that these score-based MIAs are
ineffective when targeting fairness-enhanced models in binary classifications.
The attack models trained to launch the MIAs degrade into simplistic threshold
models, resulting in lower attack performance. Meanwhile, we observe that
fairness methods often lead to prediction performance degradation for the
majority subgroups of the training data. This raises the barrier to successful
attacks and widens the prediction gaps between member and non-member data.
Building upon these insights, we propose an efficient MIA method against
fairness-enhanced models based on fairness discrepancy results (FD-MIA). It
leverages the difference in the predictions from both the original and
fairness-enhanced models and exploits the observed prediction gaps as attack
clues. We also explore potential strategies for mitigating privacy leakages.
Extensive experiments validate our findings and demonstrate the efficacy of the
proposed method.
