  Documents as short as a single sentence may inadvertently reveal sensitive
information about their authors, including e.g. their gender or ethnicity.
Style transfer is an effective way of transforming texts in order to remove any
information that enables author profiling. However, for a number of current
state-of-the-art approaches the improved privacy is accompanied by an
undesirable drop in the down-stream utility of the transformed data. In this
paper, we propose a simple, zero-shot way to effectively lower the risk of
author profiling through multilingual back-translation using off-the-shelf
translation models. We compare our models with five representative text style
transfer models on three datasets across different domains. Results from both
an automatic and a human evaluation show that our approach achieves the best
overall performance while requiring no training data. We are able to lower the
adversarial prediction of gender and race by up to $22\%$ while retaining
$95\%$ of the original utility on downstream tasks.
