PILLAR: How to make semi-private learning more effective
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In Semi-Supervised Semi-Private (SP) learning, the learner has access to both public unlabelled and private labelled data. We propose a computationally efficient algorithm that, under mild assumptions on the data, provably achieves significantly lower private labelled sample complexity and can be efficiently run on real-world datasets. For this purpose, we leverage the features extracted by networks pre-trained on public (labelled or unlabelled) data, whose distribution can significantly differ from the one on which SP learning is performed. To validate its empirical effectiveness, we propose a wide variety of experiments under tight privacy constraints (ϵ = 0.1) and with a focus on low-data regimes. In all of these settings, our algorithm exhibits significantly improved performance over available baselines that use similar amounts of public data.
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