Privatized text rewriting with local differential privacy (LDP) is a recent approach that enables sharing of sensitive textual documents while formally guaranteeing privacy protection to individuals. However, existing systems face several issues, such as formal mathematical flaws, unrealistic privacy guarantees, privatization of only individual words, as well as a lack of transparency and reproducibility. In this paper, we propose a new system 'DP-BART' that largely outperforms existing LDP systems. Our approach uses a novel clipping method, iterative pruning, and further training of internal representations which drastically reduces the amount of noise required for DP guarantees. We run experiments on five textual datasets of varying sizes, rewriting them at different privacy guarantees and evaluating the rewritten texts on downstream text classification tasks. Finally, we thoroughly discuss the privatized text rewriting approach and its limitations, including the problem of the strict text adjacency constraint in the LDP paradigm that leads to the high noise requirement.

本研究提出了一种名为'DP-BART'的新系统，可以通过新的剪裁方法，迭代修剪和对内部表示的进一步训练，大幅减少DP维度中噪声的数量，实现了较好的效果，并在五个不同规模的文本数据集上进行了实验以及评估重写文本的下游文本分类任务，同时还详细讨论了文本重写方法及其限制。

局部差分隐私下的文本重写 DP-BART