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，其在生成逼真图像、文本和数据模式方面展现出卓越能力。然而，这些进展也伴随着对数据隐私和版权侵权问题的日益关注，这主要源于模型训练对大规模数据集的依赖。差分隐私、机器遗忘和数据投毒等传统方法仅为这些复杂问题提供了零散的解决方案。本文深入探讨了数据生命周期中隐私与版权保护的多方面挑战。我们倡导将技术创新与伦理前瞻相结合的综合性方法，通过基于生命周期视角的调查和方案设计，整体性地应对这些问题。本工作旨在推动更广泛的讨论，并激励各方共同努力维护生成式人工智能中的数据隐私与版权完整性。