3 citations · 5 across the 4 of their papers we have counts for
5 papers
(Token-Level) InfoRMIA: Stronger Membership Inference and Memorization Assessment for LLMs
Jiashu Tao, Reza Shokri
Machine learning models are known to leak sensitive information, as they inevitably memorize (parts of) their training data. More alarmingly, large language models (LLMs) are now t…
Machine Learning from Explanations
Jiashu Tao, Reza Shokri
Acquiring and training on large-scale labeled data can be impractical due to cost constraints. Additionally, the use of small training datasets can result in considerable variabili…
Range Membership Inference Attacks
Jiashu Tao, Reza Shokri
Machine learning models can leak private information about their training data. The standard methods to measure this privacy risk, based on membership inference attacks (MIAs), onl…
Low-Cost High-Power Membership Inference Attacks
Sajjad Zarifzadeh, Philippe Liu, Reza Shokri
Membership inference attacks aim to detect if a particular data point was used in training a model. We design a novel statistical test to perform robust membership inference attack…
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via -Differential Privacy
Chendi Wang, Buxin Su, Jiayuan Ye +2
Differentially private (DP) machine learning algorithms incur many sources of randomness, such as random initialization, random batch subsampling, and shuffling. However, such rand…