Generalised Differential Privacy for Text Document Processing
We address the problem of how to obfuscate texts by removing stylistic clues which can identify authorship, whilst preserving (as much as possible) the content of the text.
In this paper we combine ideas from generalised differential privacy and machine learning techniques for text processing to model privacy for text documents. We define a privacy mechanism that operates at the level of text documents represented as “bags-of-words” - these representations are typical in machine learning and contain sufficient information to carry out many kinds of classification tasks including topic identification and authorship attribution (of the original documents). We show that our mechanism satisfies privacy with respect to a metric for semantic similarity, thereby providing a balance between utility, defined by the semantic content of texts, with the obfuscation of stylistic clues.
We demonstrate our implementation on a fan fiction dataset, confirming that it is indeed possible to disguise writing style effectively whilst preserving enough information for accurate content classification tasks.
Thu 11 AprDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
14:00 - 16:00 | |||
14:00 30mTalk | Wys*: A DSL for Verified Secure Multi-party Computations POST Aseem Rastogi Microsoft Research, Nikhil Swamy Microsoft Research, Michael Hicks University of Maryland, College Park Link to publication | ||
14:30 30mTalk | Generalised Differential Privacy for Text Document Processing POST Link to publication | ||
15:00 30mTalk | Symbolic verification of distance bounding protocols POST Link to publication | ||
15:30 30mTalk | On the formalisation of Σ-Protocols and Commitment Schemes POST Link to publication |