Membership Inference Attacks
Discover key takeaways from 3 podcast episodes about this topic.
Machine Text Detectors are Membership Inference Attacks
This research reveals that machine text detection and membership inference attacks, traditionally studied as separate problems, are fundamentally linked both theoretically and empirically, sharing optimal methods and exhibiting high cross-task transferability.
Worst-Case Membership Inference of Language Models
This talk introduces a novel, highly effective strategy for generating 'canaries' to audit language models for membership inference, revealing a critical disconnect between audit success and actual privacy risk.
Disparate Privacy Risks from Medical AI - An Investigation into Patient-level Privacy Risk
Medical AI models, especially larger ones, expose individual patient data to significant and disproportionately high privacy risks, particularly for minority patient groups, despite appearing safe in aggregate metrics.