A Comprehensive Analysis of Information Leakage in Deep Transfer Learning

Transfer learning is widely used for transferring knowledge from a source domain to the target domain where the labeled data is scarce . However, the source and target datasets usually belong to two different organizations in many real-world scenarios . In this study, to thoroughly analyze the potential privacy leakage in deep transfer learning, we first divide previous methods into three categories . We demonstrate specific threats that lead to unintentional privacy leakage . Additionally, we also provide some solutions to prevent these threats . To the best of our knowledge, our study is the first to . provide a thorough analysis of the information leakage issues in deep . transfer learning methods and . provide potential solutions to the issue. Extensive experiments on two public datasets and an industry dataset are conducted to show the privacy leakage under different deep transfer training settings and defense solution effectiveness. Extender experiments are conducted

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Keywords : transfer - leakage - deep - learning - privacy -

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