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arXiv · 2101.12736

N-grams Bayesian Differential Privacy

Osman Ramadan·James Withers·Douglas Orr
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Abstract

Differential privacy has gained popularity in machine learning as a strong privacy guarantee, in contrast to privacy mitigation techniques such as k-anonymity. However, applying differential privacy to n-gram counts significantly degrades the utility of derived language models due to their large vocabularies. We propose a differential privacy mechanism that uses public data as a prior in a Bayesian setup to provide tighter bounds on the privacy loss metric epsilon, and thus better privacy-utility trade-offs. It first transforms the counts to log space, approximating the distribution of the public and private data as Gaussian. The posterior distribution is then evaluated and softmax is applied to produce a probability distribution. This technique achieves up to 85% reduction in KL divergence compared to previously known mechanisms at epsilon equals 0.1. We compare our mechanism to k-anonymity in a n-gram language modelling task and show that it offers competitive performance at large vocabulary sizes, while also providing superior privacy protection.

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Osman Ramadan, James Withers, Douglas Orr. 2021-01-29. N-grams Bayesian Differential Privacy. https://arxiv.org/abs/2101.12736

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