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arXiv · 2108.02831

Differentially Private n-gram Extraction

Kunho Kim·Sivakanth Gopi·Janardhan Kulkarni·Sergey Yekhanin
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Abstract

We revisit the problem of $n$-gram extraction in the differential privacy setting. In this problem, given a corpus of private text data, the goal is to release as many $n$-grams as possible while preserving user level privacy. Extracting $n$-grams is a fundamental subroutine in many NLP applications such as sentence completion, response generation for emails etc. The problem also arises in other applications such as sequence mining, and is a generalization of recently studied differentially private set union (DPSU). In this paper, we develop a new differentially private algorithm for this problem which, in our experiments, significantly outperforms the state-of-the-art. Our improvements stem from combining recent advances in DPSU, privacy accounting, and new heuristics for pruning in the tree-based approach initiated by Chen et al. (2012).

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Kunho Kim, Sivakanth Gopi, Janardhan Kulkarni, Sergey Yekhanin. 2021-08-05. Differentially Private n-gram Extraction. https://arxiv.org/abs/2108.02831

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