Advancing Privacy Standards and Evaluation Metrics for Robust AI Model Deployment

Abstract

The transformative potential of generative AI, encompassing large language models (LLMs) and generative adversarial networks (GANs), is undeniable, revolutionizing content creation, automation, and AI applications.  However, this rapid progress is accompanied by escalating security and privacy vulnerabilities.  The very capabilities that make these models so powerful also create opportunities for malicious exploitation, including data leakage, adversarial attacks, and the exposure of sensitive information.  This paper delves into the critical area of privacy-preserving techniques for generative AI, exploring promising solutions such as differential privacy, federated learning, and homomorphic encryption.  We analyze the landscape of emerging security threats, including model inversion attacks aimed at reconstructing training data, and prompt injection attacks designed to manipulate model behavior.  Furthermore, we review the evolving landscape of data privacy standards and regulations, with a particular focus on the General Data Protection Regulation (GDPR), to understand the legal and ethical implications of deploying generative AI.  A key contribution of this work is the proposal of secure deployment practices, offering practical guidelines for mitigating privacy risks throughout the generative AI lifecycle.  This paper aims to establish a robust and comprehensive framework for evaluating and addressing privacy risks, ensuring compliance with relevant regulations, and ultimately promoting the responsible and secure utilization of generative AI across diverse industries.  By addressing these critical challenges, we can harness the immense potential of generative AI while safeguarding individual privacy and fostering trust in these powerful technologies.