  The amount of data for processing and categorization grows at an ever
increasing rate. At the same time the demand for collaboration and transparency
in organizations, government and businesses, drives the release of data from
internal repositories to the public or 3rd party domain. This in turn increase
the potential of sharing sensitive information. The leak of sensitive
information can potentially be very costly, both financially for organizations,
but also for individuals. In this work we address the important problem of
sensitive information detection. Specially we focus on detection in
unstructured text documents.
  We show that simplistic, brittle rule sets for detecting sensitive
information only find a small fraction of the actual sensitive information.
Furthermore we show that previous state-of-the-art approaches have been
implicitly tailored to such simplistic scenarios and thus fail to detect actual
sensitive content. We develop a novel family of sensitive information detection
approaches which only assumes access to labeled examples, rather than
unrealistic assumptions such as access to a set of generating rules or
descriptive topical seed words. Our approaches are inspired by the current
state-of-the-art for paraphrase detection and we adapt deep learning approaches
over recursive neural networks to the problem of sensitive information
detection. We show that our context-based approaches significantly outperforms
the family of previous state-of-the-art approaches for sensitive information
detection, so-called keyword-based approaches, on real-world data and with
human labeled examples of sensitive and non-sensitive documents.
