We propose a Neighbourhood-Aware Differential Privacy (NADP) mechanism considering the neighbourhood of a word in a pretrained static word embedding space to determine the minimal amount of noise required to guarantee a specified privacy level. We first construct a nearest neighbour graph over the words using their embeddings, and factorise it into a set of connected components (i.e. neighbourhoods). We then separately apply different levels of Gaussian noise to the words in each neighbourhood, determined by the set of words in that neighbourhood. Experiments show that our proposed NADP mechanism consistently outperforms multiple previously proposed DP mechanisms such as Laplacian, Gaussian, and Mahalanobis in multiple downstream tasks, while guaranteeing higher levels of privacy.

翻译：我们提出了一种邻域感知差分隐私（NADP）机制，该机制通过考虑预训练静态词嵌入空间中某个词的邻域，来确定保证特定隐私级别所需的最小噪声量。我们首先利用词嵌入构建词的最近邻图，并将其分解为一组连通分量（即邻域）。然后，我们根据每个邻域内的词集合，分别对该邻域中的词施加不同级别的高斯噪声。实验表明，我们所提出的NADP机制在多个下游任务中始终优于先前提出的多种差分隐私机制（如拉普拉斯、高斯和马氏距离），同时保证了更高的隐私保护级别。