  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.
