Use fewer instances of the letter "i": Toward writing style anonymization

Andrew W.E. McDonald, Sadia Afroz, Aylin Caliskan, Ariel Stolerman, Rachel Greenstadt

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    This paper presents Anonymouth, a novel framework for anonymizing writing style. Without accounting for style, anonymous authors risk identification. This framework is necessary to provide a tool for testing the consistency of anonymized writing style and a mechanism for adaptive attacks against stylometry techniques. Our framework defines the steps necessary to anonymize documents and implements them. A key contribution of this work is this framework, including novel methods for identifying which features of documents need to change and how they must be changed to accomplish document anonymization. In our experiment, 80% of the user study participants were able to anonymize their documents in terms of a fixed corpus and limited feature set used. However, modifying pre-written documents were found to be difficult and the anonymization did not hold up to more extensive feature sets. It is important to note that Anonymouth is only the first step toward a tool to acheive stylometric anonymity with respect to state-of-the-art authorship attribution techniques. The topic needs further exploration in order to accomplish significant anonymity.

    Original languageEnglish (US)
    Title of host publicationPrivacy Enhancing Technologies - 12th International Symposium, PETS 2012, Proceedings
    Number of pages20
    StatePublished - 2012
    Event12th International Symposium on Privacy Enhancing Technologies, PETS 2012 - Vigo, Spain
    Duration: Jul 11 2012Jul 13 2012

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume7384 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference12th International Symposium on Privacy Enhancing Technologies, PETS 2012


    • anonymity
    • machine learning
    • privacy
    • stylometry

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science


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