Are there any conditions under which a generative model's outputs are guaranteed not to infringe the copyrights of its training data? This is the question of "provable copyright protection" first posed by Vyas, Kakade, and Barak (ICML 2023). They define near access-freeness (NAF) and propose it as sufficient for protection. This paper revisits the question and establishes new foundations for provable copyright protection -- foundations that are firmer both technically and legally. First, we show that NAF alone does not prevent infringement. In fact, NAF models can enable verbatim copying, a blatant failure of copy protection that we dub being tainted. Then, we introduce our blameless copy protection framework for defining meaningful guarantees, and instantiate it with clean-room copy protection. Clean-room copy protection allows a user to control their risk of copying by behaving in a way that is unlikely to copy in a counterfactual clean-room setting. Finally, we formalize a common intuition about differential privacy and copyright by proving that DP implies clean-room copy protection when the dataset is golden, a copyright deduplication requirement.

翻译：是否存在某些条件，使得生成模型的输出能够保证不侵犯其训练数据的版权？这是Vyas、Kakade和Barak（ICML 2023）首次提出的“可证明版权保护”问题。他们定义了近访问自由性（NAF），并提议将其作为保护的充分条件。本文重新审视这一问题，并为可证明版权保护建立了新的理论基础——这些基础在技术和法律层面都更为坚实。首先，我们证明仅凭NAF无法防止侵权。事实上，NAF模型可能促成逐字复制，这是一种明显的复制保护失效，我们称之为“受污染”。接着，我们引入了无责复制保护框架来定义有意义的保证，并通过洁净室复制保护实现该框架。洁净室复制保护允许用户通过一种在反事实洁净室环境中不太可能复制的方式行事，从而控制其复制风险。最后，我们通过证明当数据集满足“黄金”条件（一种版权去重要求）时，差分隐私（DP）蕴含洁净室复制保护，从而形式化了关于差分隐私与版权的常见直觉。