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AI的出现标志着人工智能领域的一个重要里程碑，其在生成逼真图像、文本和数据模式方面展现出卓越的能力。然而，这些进步也因模型训练依赖海量数据集而加剧了对数据隐私和版权侵权的担忧。差分隐私、机器遗忘和数据投毒等传统方法仅能为这些复杂问题提供零散的解决方案。本文深入探讨了数据生命周期中隐私与版权保护所面临的多方面挑战。我们倡导将技术创新与伦理预判相结合的整体方法，通过从生命周期视角出发研究和设计解决方案，全面应对这些关切。本研究旨在推动更广泛的讨论，并激发业界为维护生成式AI中的数据隐私与版权完整性而共同努力。