Risks of Large Language Models: Privacy Concerns Unveiled #AIprivacy

Generative AI Privacy Risks. Privacy Risks of Large Language Models… | by Debmalya Biswas | Jul, 2024

This article discusses the privacy risks associated with Large Language Models (LLMs) in enterprises. It highlights the trend of applying traditional privacy frameworks designed for data science pipelines to LLM use-cases, which is deemed inefficient and risky. The need to adapt enterprise privacy frameworks to address the unique privacy aspects of LLMs is emphasized.

The article also delves into privacy attack scenarios in traditional supervised machine learning contexts, focusing on prediction or classification tasks. It outlines two main categories of inference attacks: membership inference and property inference. The need for enterprises to update their privacy frameworks, checklists, and tools to accommodate the novel privacy challenges posed by LLMs is underscored.

Additionally, the article features visual aids comparing the privacy risks of Gen AI and traditional machine learning models. It stresses the importance of recognizing and addressing the distinct privacy concerns associated with LLMs, urging enterprises to evolve their privacy strategies accordingly. Overall, the article serves as a call to action for organizations to enhance their privacy measures in response to the deployment of LLMs in various business contexts.

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