Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Abbasi, A., Chen, H.: Writeprints: A stylometric approach to identity-level identification and similarity detection in cyberspace. ACM Trans. Inf. Syst. 26(2), 1–29 (2008)
Narayanan, A., Paskov, H., Gong, N., Bethencourt, J., Stefanov, E., Shin, R., Song, D.: On the feasibility of internet-scale author identification. In: Proceedings of the 33rd Conference on IEEE Symposium on Security and Privacy. IEEE (2012)
Wayman, J., Orlans, N., Hu, Q., Goodman, F., Ulrich, A., Valencia, V.: Technology assessment for the state of the art biometrics excellence roadmap (March 2009), http://www.biometriccoe.gov/SABER/index.htm
Brennan, M., Greenstadt, R.: Practical attacks against authorship recognition techniques. In: Proceedings of the Twenty-First Innovative Applications of Artificial Intelligence Conference (2009)
Afroz, S., Brennan, M., Greenstadt, R.: Detecting hoaxes, frauds, and deception in writing style online. In: Proceedings of the 33rd Conference on IEEE Symposium on Security and Privacy. IEEE (2012)
Eckersley, P.: How Unique Is Your Web Browser? In: Atallah, M.J., Hopper, N.J. (eds.) PETS 2010. LNCS, vol. 6205, pp. 1–18. Springer, Heidelberg (2010)
Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: Proceedings of the 29th IEEE Symposium on Security and Privacy, pp. 111–125. IEEE (2008)
Calandrino, J.A., Kilzer, A., Narayanan, A., Felten, E.W., Shmatikov, V.: You might also like: Privacy risks of collaborative filtering. In: Proceedings of the 32nd IEEE Symposium on Security and Privacy, pp. 231–246. IEEE (2011)
Dingledine, R., Mathewson, N., Syverson, P.: Tor: The second-generation onion router. In: Proceedings of the 13th Conference on USENIX Security Symposium, vol. 13, pp. 21–21. USENIX Association (2004)
Rao, J.R., Rohatgi, P.: Can pseudonymity really guarantee privacy. In: Proceedings of the Ninth USENIX Security Symposium, pp. 85–96 (2000)
Kacmarcik, G., Gamon, M.: Obfuscating document stylometry to preserve author anonymity. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, pp. 444–451. Association for Computational Linguistics (2006)
Juola, P.: Authorship attribution. Foundations and Trends in information Retrieval 1(3), 233–334 (2008)
Uzuner, Ö., Katz, B.: A Comparative Study of Language Models for Book and Author Recognition. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS (LNAI), vol. 3651, pp. 969–980. Springer, Heidelberg (2005)
Juola, P.: Jgaap, a java-based, modular, program for textual analysis, text categorization, and authorship attribution
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)
Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, Philadelphia, PA, USA, pp. 1027–1035 (2007)
Clark, J.H., Hannon, C.J.: A Classifier System for Author Recognition Using Synonym-Based Features. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS (LNAI), vol. 4827, pp. 839–849. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
McDonald, A.W.E., Afroz, S., Caliskan, A., Stolerman, A., Greenstadt, R. (2012). Use Fewer Instances of the Letter “i”: Toward Writing Style Anonymization. In: Fischer-Hübner, S., Wright, M. (eds) Privacy Enhancing Technologies. PETS 2012. Lecture Notes in Computer Science, vol 7384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31680-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-31680-7_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31679-1
Online ISBN: 978-3-642-31680-7
eBook Packages: Computer ScienceComputer Science (R0)