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We propose a Neighbourhood-Aware Differential Privacy (NADP) mechanismconsidering the neighbourhood of a word in a pretrained static word embeddingspace to determine the minimal amount of noise required to guarantee aspecified privacy level. We first construct a nearest neighbour graph over thewords using their embeddings, and factorise it into a set of connectedcomponents (i.e. neighbourhoods). We then separately apply different levels ofGaussian noise to the words in each neighbourhood, determined by the set ofwords in that neighbourhood. Experiments show that our proposed NADP mechanismconsistently outperforms multiple previously proposed DP mechanisms such asLaplacian, Gaussian, and Mahalanobis in multiple downstream tasks, whileguaranteeing higher levels of privacy.

A Neighbourhood-Aware Differential Privacy Mechanism for Static Word Embeddings