How to talk about LLM privacy: a guide for data scientists
How to talk about LLM privacy: a guide for data scientists
Photo by Pablo García Saldaña on UnsplashLately, there are tons of explainers on how to use LLMs, or position pieces on why they are great or terrible. This article is none of those. This is for data scientists and machine learning experts who already know LLMs, but need to discuss privacy concerns more broadly.I’m your privacy engineering friend, and I’ll give you some language for discussing privacy issues around LLM with anyone. You’ll be able to explain the issues in non-technical terms with lawyers, CEOs, and any part of your company. It can even help with conversations outside of work, if friends and relatives start asking about what could go wrong with models. This article will help you translate technical ML terms into clear privacy implications.A Simple Dichotomy of Privacy Concerns from LLMFor simplicity, let’s think of privacy concerns from LLM in two main categories: models share correct information about people, and models share incorrect information about people.Models share correct information about people:As a machine learning person, you may already be familiar with memorization and membership inference attacks. Both of these attacks allow models to share information about someone that they may not…