Discriminative Adversarial Privacy: Balancing Accuracy and Membership Privacy in Neural Networks
Eugenio Lomurno (Politecnico di Milano),* Alberto Archetti (Politecnico di Milano), Francesca Ausonio (Politecnico di Milano), Matteo Matteucci (Politecnico di Milano)
The 34th British Machine Vision Conference
Abstract
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.Video
Citation
@inproceedings{Lomurno_2023_BMVC,
author = {Eugenio Lomurno and Alberto Archetti and Francesca Ausonio and Matteo Matteucci},
title = {Discriminative Adversarial Privacy: Balancing Accuracy and Membership Privacy in Neural Networks},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0799.pdf}
}
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