Researchmembership inferencedeep learningml frameworksprivacy preserving
Researchers Demonstrate Membership Inference Facilitation Method
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At NDSS 2025, Zitao Chen and Karthik Pattabiraman (University of British Columbia) present a paper proposing a method to facilitate membership inference attacks against deep learning models. The authors argue that widespread ML frameworks and code repositories make it easier for non-experts to build high-performance models on sensitive data, increasing privacy risks for data such as clinical records. The work underscores the need for stronger privacy evaluations and defenses.
