Megha Khosla

A Review of Anonymization for Healthcare Data

2022 · Big Data

Mining health data can lead to faster medical decisions, improvement in the quality of treatment, disease prevention, and reduced cost, and it drives innovative solutions within the healthcare sector. However, health data are highly sensitive …

Private Graph Extraction via Feature Explanations

2023 · Proceedings on Privacy Enhancing Technologies

Privacy and interpretability are two important ingredients for achieving trustworthy machine learning. We study the interplay of these two aspects in graph machine learning through graph reconstruction attacks. The goal of the adversary here is …

DINE: Dimensional Interpretability of Node Embeddings

2023 · arXiv (Cornell University)

Graphs are ubiquitous due to their flexibility in representing social and technological systems as networks of interacting elements. Graph representation learning methods, such as node embeddings, are powerful approaches to map nodes into a latent …

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification

2024 · arXiv (Cornell University)

Graph neural networks (GNNs) have emerged as powerful models for learning representations of graph data showing state of the art results in various tasks. Nevertheless, the superiority of these methods is usually supported by either …