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.

翻译：生成式人工智能的出现标志着人工智能领域的一个重要里程碑，展示了在生成逼真图像、文本和数据模式方面的卓越能力。然而，这些进步伴随着对数据隐私和版权侵权的高度关注，这主要是由于模型训练依赖于海量数据集。差分隐私、机器遗忘和数据投毒等传统方法仅能提供针对这些复杂问题的碎片化解决方案。本文深入探讨了数据生命周期中隐私与版权保护的多方面挑战。我们倡导将技术创新与伦理前瞻性相结合的综合方法，通过基于生命周期视角的调查与方案设计，全面应对这些关切。本研究旨在推动更广泛的讨论，并激发在生成式人工智能中维护数据隐私与版权完整性的协同努力。