Privacy in Foundation Models: A Conceptual Framework for System Design

• URL: http://arxiv.org/abs/2311.06998v2
• Date: Tue, 10 Dec 2024 09:35:27 GMT
• Title: Privacy in Foundation Models: A Conceptual Framework for System Design
• Authors: Tingting Bi, Guangsheng Yu, Qin Wang,
• Abstract summary: Foundation models present both significant challenges and incredible opportunities. There is currently a lack of consensus regarding the comprehensive scope of both technical and non-technical issues that the privacy evaluation process should encompass. This paper introduces a novel conceptual framework that integrates various responsible AI patterns from multiple perspectives.
• Score: 3.438211531047665
• License:
• Abstract: AI and its relevant technologies, including machine learning, deep learning, chatbots, virtual assistants, and others, are currently undergoing a profound transformation of development and organizational processes within companies. Foundation models present both significant challenges and incredible opportunities. In this context, ensuring the quality attributes of foundation model-based systems is of paramount importance, and with a particular focus on the challenging issue of privacy due to the sensitive nature of the data and information involved. However, there is currently a lack of consensus regarding the comprehensive scope of both technical and non-technical issues that the privacy evaluation process should encompass. Additionally, there is uncertainty about which existing methods are best suited to effectively address these privacy concerns. In response to this challenge, this paper introduces a novel conceptual framework that integrates various responsible AI patterns from multiple perspectives, with the specific aim of safeguarding privacy.

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