UPTON: Preventing Authorship Leakage from Public Text Release via Data Poisoning

Ziyao Wang, Thai Le, Dongwon Lee

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

2 Scopus citations

Abstract

Consider a scenario where an author-e.g., activist, whistle-blower, with many public writings wishes to write “anonymously" when attackers may have already built an authorship attribution (AA) model based off of public writings including those of the author. To enable her wish, we ask a question “can one make the publicly released writings, T, unattributable so that AA models trained on T cannot attribute its authorship well?" Toward this question, we present a novel solution, UPTON, that exploits black-box data poisoning methods to weaken the authorship features in training samples and make released texts unlearnable. It is different from previous obfuscation works-e.g., adversarial attacks that modify test samples or backdoor works that only change the model outputs when triggering words occur. Using four authorship datasets (IMDb10, IMDb64, Enron and WJO), we present empirical validation where UPTON successfully downgrades the accuracy of AA models to the impractical level (∼35%) while keeping texts still readable (semantic similarity>0.9). UPTON remains effective to AA models that are already trained on available clean writings of authors.

Original languageEnglish (US)
Title of host publicationFindings of the Association for Computational Linguistics
Subtitle of host publicationEMNLP 2023
PublisherAssociation for Computational Linguistics (ACL)
Pages11952-11965
Number of pages14
ISBN (Electronic)9798891760615
StatePublished - 2023
Event2023 Findings of the Association for Computational Linguistics: EMNLP 2023 - Singapore, Singapore
Duration: Dec 6 2023Dec 10 2023

Publication series

NameFindings of the Association for Computational Linguistics: EMNLP 2023

Conference

Conference2023 Findings of the Association for Computational Linguistics: EMNLP 2023
Country/TerritorySingapore
CitySingapore
Period12/6/2312/10/23

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems
  • Language and Linguistics
  • Linguistics and Language

Cite this