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).<\/p>\n","protected":false},"excerpt":{"rendered":"
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 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