Artificial intelligence (AI) models introduce privacy vulnerabilities to systems. These vulnerabilities may impact model owners or system users; they exist during model development, deployment, and inference phases, and threats can be internal or external to the system. In this paper, we investigate potential threats and propose the use of several privacy-enhancing technologies (PETs) to defend AI-enabled systems. We then provide a framework for PETs evaluation for a AI-enabled systems and discuss the impact PETs may have on system-level variables.

翻译：人工智能模型为系统引入了隐私漏洞。这些漏洞可能影响模型所有者或系统用户；它们存在于模型开发、部署和推理阶段，威胁可能来自系统内部或外部。在本文中，我们研究了潜在威胁，并提出使用多种隐私增强技术来防御人工智能系统。随后，我们提供了一个面向人工智能系统的隐私增强技术评估框架，并讨论了隐私增强技术可能对系统级变量产生的影响。