Session 12C: Membership Inference
Authors, Creators & Presenters: Zitao Chen (University of British Columbia), Karthik Pattabiraman (University of British Columbia)
PAPER
A Method to Facilitate Membership Inference Attacks in Deep Learning Models
Modern machine learning (ML) ecosystems offer a surging number of ML frameworks and code repositories that can greatly facilitate the development of ML models. Today, even ordinary data holders who are not ML experts can apply off-the-shelf codebase to build high-performance ML models on their data, many of which are sensitive in nature (e.g., clinical records).
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The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.
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Marc Handelman
Source: Security Boulevard
Source Link: https://securityboulevard.com/2026/02/ndss-2025-a-method-to-facilitate-membership-inference-attacks-in-deep-learning-models/