Differentially Private n-gram Extraction

• URL: http://arxiv.org/abs/2108.02831v1
• Date: Thu, 5 Aug 2021 19:53:16 GMT
• Title: Differentially Private n-gram Extraction
• Authors: Kunho Kim, Sivakanth Gopi, Janardhan Kulkarni and Sergey Yekhanin
• Abstract summary: 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. We develop a new differentially private algorithm for this problem which, in our experiments, significantly outperforms the state-of-the-art.
• Score: 19.401898070938593
• License: http://creativecommons.org/licenses/by/4.0/
• 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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