Revisiting Membership Inference Under Realistic Assumptions

21 May 2020Bargav JayaramanLingxiao WangDavid EvansQuanquan Gu

Membership inference attacks on models trained using machine learning have been shown to pose significant privacy risks. However, previous works on membership inference assume a balanced prior distribution where the adversary randomly chooses target records from a pool that has equal numbers of members and non-members... (read more)

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