Discriminative Adversarial Privacy: Balancing Accuracy and Membership
  Privacy in Neural Networks
Eugenio Lomurno Alberto Archetti Francesca Ausonio Matteo Matteucci
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.