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Found 5 papers, 2 papers with code
SoK: Reducing the Vulnerability of Fine-tuned Language Models to Membership Inference Attacks
We provide the first systematic review of the vulnerability of fine-tuned large language models to membership inference attacks, the various factors that come into play, and the effectiveness of different defense strategies.
Improved Membership Inference Attacks Against Language Classification Models
Assessing the privacy risks of machine learning models is crucial to enabling knowledgeable decisions on whether to use, deploy, or share a model.
Data Minimization for GDPR Compliance in Machine Learning Models
The EU General Data Protection Regulation (GDPR) mandates the principle of data minimization, which requires that only data necessary to fulfill a certain purpose be collected.
Anonymizing Machine Learning Models
Anonymized data, however, is exempt from the obligations set out in these regulations.
Reducing Risk of Model Inversion Using Privacy-Guided Training
These attacks are able to reveal the values of certain sensitive features of individuals who participated in training the model.
