根据词元重要性和隐私敏感度动态分配隐私预算，提升文本隐私保护效果。
STAMP: Selective Task-Aware Mechanism for Text Privacy
- 按任务重要性与隐私敏感度选择性分配噪声，实现细粒度隐私控制
- 采用极坐标扰动机制，在保持语义邻近性的同时增强下游任务性能
- 适用于需要高隐私保障的问答、评论和新闻分类场景
我们提出STAMP（Selective Task-Aware Mechanism for Text Privacy），一种新的任务感知文本隐私化框架，可实现更优的隐私-效用权衡。STAMP通过联合考虑（i）每个词元对下游任务的重要性（基于任务或查询特定表示），以及（ii）其隐私敏感度（如姓名、日期、标识符），在词元层面选择性分配隐私预算。这种细粒度的分组控制允许对输入不同部分施加不同程度的噪声，平衡隐私保护与任务相关性。为私有化单个词元嵌入，我们引入极坐标机制（polar mechanism），仅扰动嵌入在单位球面上的方向，同时保留其模长。解码通过余弦最近邻搜索完成，使扰动几何与解码几何一致。相比各向同性噪声机制，极坐标机制能更好保持嵌入空间中的语义邻近性，显著提升下游任务效用。在SQuAD、Yelp和AG News数据集上的实验表明，当结合归一化极坐标机制时，STAMP在不同词元隐私预算下均持续实现更优的隐私-效用权衡。
原文摘要 · Abstract (English)
We present STAMP (Selective Task-Aware Mechanism for Text Privacy), a new framework for task-aware text privatization that achieves an improved privacy-utility trade-off. STAMP selectively allocates privacy budgets across tokens by jointly considering (i) each token's importance to the downstream task (as measured via a task- or query-specific representation), and (ii) its privacy sensitivity (e.g., names, dates, identifiers). This token-level partitioning enables fine-grained, group-wise control over the level of noise applied to different parts of the input, balancing privacy protection with task relevance. To privatize individual token embeddings, we introduce the polar mechanism, which perturbs only the direction of embeddings on the unit sphere while preserving their magnitude. Decoding is performed via cosine nearest-neighbor search, aligning the perturbation geometry with the decoding geometry. Unlike isotropic noise mechanisms, the polar mechanism maintains semantic neighborhoods in the embedding space and better preserves downstream utility. Experimental evaluations on SQuAD, Yelp, and AG News datasets demonstrate that STAMP, when combined with the normalized polar mechanism, consistently achieves superior privacy-utility trade-offs across varying per-token privacy budgets.
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