Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective

TL;DR

This paper examines privacy and copyright challenges in Generative AI throughout the data lifecycle, proposing integrated solutions that combine technical and ethical strategies to address these complex issues.

Contribution

It introduces a comprehensive lifecycle perspective on privacy and copyright protection, advocating for holistic, integrated approaches beyond traditional fragmented methods.

Findings

Highlights limitations of existing privacy techniques

Proposes a holistic framework for data protection

Encourages ethical and technical integration

Abstract

The advent of Generative AI has marked a significant milestone in artificial intelligence, demonstrating remarkable capabilities in generating realistic images, texts, and data patterns. However, these advancements come with heightened concerns over data privacy and copyright infringement, primarily due to the reliance on vast datasets for model training. Traditional approaches like differential privacy, machine unlearning, and data poisoning only offer fragmented solutions to these complex issues. Our paper delves into the multifaceted challenges of privacy and copyright protection within the data lifecycle. We advocate for integrated approaches that combines technical innovation with ethical foresight, holistically addressing these concerns by investigating and devising solutions that are informed by the lifecycle perspective. This work aims to catalyze a broader discussion and inspire concerted efforts towards data privacy and copyright integrity in Generative AI.