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arXiv · 2006.11601

Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks

Lixin Fan·Kam Woh Ng·Ce Ju·Tianyu Zhang·Chang Liu·Chee Seng Chan·Qiang Yang
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Abstract

This paper investigates capabilities of Privacy-Preserving Deep Learning (PPDL) mechanisms against various forms of privacy attacks. First, we propose to quantitatively measure the trade-off between model accuracy and privacy losses incurred by reconstruction, tracing and membership attacks. Second, we formulate reconstruction attacks as solving a noisy system of linear equations, and prove that attacks are guaranteed to be defeated if condition (2) is unfulfilled. Third, based on theoretical analysis, a novel Secret Polarization Network (SPN) is proposed to thwart privacy attacks, which pose serious challenges to existing PPDL methods. Extensive experiments showed that model accuracies are improved on average by 5-20% compared with baseline mechanisms, in regimes where data privacy are satisfactorily protected.

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Lixin Fan, Kam Woh Ng, Ce Ju, Tianyu Zhang, Chang Liu, Chee Seng Chan, Qiang Yang. 2020-06-20. Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks. https://arxiv.org/abs/2006.11601

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