Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective
This work addresses privacy and copyright issues for stakeholders in Generative AI, but it is incremental as it builds on existing fragmented solutions.
The paper tackles the problem of data privacy and copyright infringement in Generative AI by advocating for integrated approaches based on a lifecycle perspective, aiming to inspire broader efforts in this area.
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.