  Text mining and information retrieval techniques have been developed to
assist us with analyzing, organizing and retrieving documents with the help of
computers. In many cases, it is desirable that the authors of such documents
remain anonymous: Search logs can reveal sensitive details about a user,
critical articles or messages about a company or government might have severe
or fatal consequences for a critic, and negative feedback in customer surveys
might negatively impact business relations if they are identified. Simply
removing personally identifying information from a document is, however,
insufficient to protect the writer's identity: Given some reference texts of
suspect authors, so-called authorship attribution methods can reidentfy the
author from the text itself.
  One of the most prominent models to represent documents in many common text
mining and information retrieval tasks is the vector space model where each
document is represented as a vector, typically containing its term frequencies
or related quantities. We therefore propose an automated text anonymization
approach that produces synthetic term frequency vectors for the input documents
that can be used in lieu of the original vectors. We evaluate our method on an
exemplary text classification task and demonstrate that it only has a low
impact on its accuracy. In contrast, we show that our method strongly affects
authorship attribution techniques to the level that they become infeasible with
a much stronger decline in accuracy. Other than previous authorship obfuscation
methods, our approach is the first that fulfills differential privacy and hence
comes with a provable plausible deniability guarantee.

Cryptography and Security;Information Retrieval