please enhance the paragraph "Previous studies have introduced fairness-friendly methods to train artificial intelligence (AI) models to address discriminatory behaviors against specific subgroups. While these models can achieve fair performance in terms of AI predictions across different subgroups, recent research has revealed their vulnerabilities to score-based membership inference attacks (MIAs), where attackers can infer sample membership from prediction scores. However, we discover that the score-based MIAs are ineffective when targeting fairness-enforced models in binary classifications. This is because the trained attack models are degraded into simple threshold models, resulting in inferior attack performance. We further find that fairness-aware AI training methods tend to introduce additional performance loss to the majority subgroups of data. This raises the barrier to successful attacks and widens the distribution gaps between member and non-member data. Based on our observations, we propose an efficient attack method against fairness-enhanced models using differential privacy assessment. It leverages predictions from the original and fairness-enhanced models, and exploits the observed distribution gaps as attack clues. We further explore potential approaches for mitigating privacy leakages. Comprehensive experiments with diverse settings validate our discoveries and demonstrate the efficacy of the proposed method. " Provide response in all capital letters, no lowercase letters allowed.
user