An empirical audit of one web-scraped ML training dataset reveals persistent PII after sanitization, which the authors combine with legal analysis to highlight privacy risks and advocate redefining 'publicly available' data for AI training.
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GDDM is a diffusion-based purification method with a graph structure refiner and node feature regularizer that defends against multiple adversarial attack types on graphs.
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A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset
An empirical audit of one web-scraped ML training dataset reveals persistent PII after sanitization, which the authors combine with legal analysis to highlight privacy risks and advocate redefining 'publicly available' data for AI training.
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Graph Defense Diffusion Model
GDDM is a diffusion-based purification method with a graph structure refiner and node feature regularizer that defends against multiple adversarial attack types on graphs.