Search Results for author:
Found 6 papers, 3 papers with code
Watermark Stealing in Large Language Models
LLM watermarking has attracted attention as a promising way to detect AI-generated content, with some works suggesting that current schemes may already be fit for deployment.
Large Language Models are Advanced Anonymizers
Recent work in privacy research on large language models has shown that they achieve near human-level performance at inferring personal data from real-world online texts.
From Principle to Practice: Vertical Data Minimization for Machine Learning
We propose a novel vertical DM (vDM) workflow based on data generalization, which by design ensures that no full-resolution client data is collected during training and deployment of models, benefiting client privacy by reducing the attack surface in case of a breach.
Beyond Memorization: Violating Privacy Via Inference with Large Language Models
In this work, we present the first comprehensive study on the capabilities of pretrained LLMs to infer personal attributes from text.
Bayesian Framework for Gradient Leakage
We demonstrate that existing leakage attacks can be seen as approximations of this optimal adversary with different assumptions on the probability distributions of the input data and gradients.
Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks
We present a new abstract interpretation framework for the precise over-approximation of numerical fixpoint iterators.
