Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption

Xuanqi Liu

Network and Distributed System Security (NDSS) Symposium 2024 · Day 3 · Privacy-Preserving ML

This article delves into "Pencil," a groundbreaking framework for collaborative machine learning that addresses critical challenges in data privacy, model confidentiality, and system extensibility. Presented at the NDSS Symposium by Xuanqi Liu, Pencil introduces a novel approach to enable multiple data owners (DOes) and a model owner (MO) to train neural networks together without compromising sensitive information. The talk highlights the growing tension between the need for large, diverse datasets to train robust AI models and stringent privacy regulations like GDPR, particularly in sectors such as anti-money laundering (AML).