The remarkable proliferation of deep learning across various industries has
underscored the importance of data privacy and security in AI pipelines. As the
evolution of sophisticated Membership Inference Attacks (MIAs) threatens the
secrecy of individual-specific information used for training deep learning
models, Differential Privacy (DP) raises as one of the most utilized techniques
to protect models against malicious attacks. However, despite its proven
theoretical properties, DP can significantly hamper model performance and
increase training time, turning its use impractical in real-world scenarios.
Tackling this issue, we present Discriminative Adversarial Privacy (DAP), a
novel learning technique designed to address the limitations of DP by achieving
a balance between model performance, speed, and privacy. DAP relies on
adversarial training based on a novel loss function able to minimise the
prediction error while maximising the MIA's error. In addition, we introduce a
novel metric named Accuracy Over Privacy (AOP) to capture the
performance-privacy trade-off. Finally, to validate our claims, we compare DAP
with diverse DP scenarios, providing an analysis of the results from
performance, time, and privacy preservation perspectives.

本文提出了一种名为 “判别性对抗隐私”（DAP）的新型学习技术，该技术通过达到模型性能、速度和隐私之间的平衡来解决 DP 的局限性。DAP 依靠对抗训练，基于一种新的损失函数，该函数能够最小化预测误差并最大化 MIA 的误差。此外，我们引入了一种名为 “准确性隐私权”（AOP）的新指标来捕捉性能 - 隐私权衡。最后，为了验证我们的观点，我们将 DAP 与不同的 DP 情景进行比较，并从性能、时间和隐私保护角度分析结果。