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Found 6 papers, 1 papers with code
Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning
Given that the right to be forgotten should be upheld for every individual, we advocate for a more rigorous evaluation of LLM unlearning methods.
Synthetic Data Applications in Finance
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality.
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models
Privacy concerns have led to a surge in the creation of synthetic datasets, with diffusion models emerging as a promising avenue.
GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?
However, these models face challenges in generating large attributed graphs due to the complex attribute-structure correlations and the large size of these graphs.
Differentially Private Synthetic Data Using KD-Trees
Creation of a synthetic dataset that faithfully represents the data distribution and simultaneously preserves privacy is a major research challenge.
Differentially Private Learning of Hawkes Processes
Hawkes processes have recently gained increasing attention from the machine learning community for their versatility in modeling event sequence data.
