Search Results for author:
Found 7 papers, 2 papers with code
Private Attribute Inference from Images with Vision-Language Models
As large language models (LLMs) become ubiquitous in our daily tasks and digital interactions, associated privacy risks are increasingly in focus.
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.
Instruction Tuning for Secure Code Generation
However, existing instruction tuning schemes overlook a crucial aspect: the security of generated code.
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.
CuTS: Customizable Tabular Synthetic Data Generation
To ensure high synthetic data quality in the presence of custom specifications, CuTS is pre-trained on the original dataset and fine-tuned on a differentiable loss automatically derived from the provided specifications using novel relaxations.
TabLeak: Tabular Data Leakage in Federated Learning
A successful attack for tabular data must address two key challenges unique to the domain: (i) obtaining a solution to a high-variance mixed discrete-continuous optimization problem, and (ii) enabling human assessment of the reconstruction as unlike for image and text data, direct human inspection is not possible.
Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering
The computational demands of neural architecture search (NAS) algorithms are usually directly proportional to the size of their target search spaces.
