Just Rewrite It Again: A Post-Processing Method for Enhanced Semantic Similarity and Privacy Preservation of Differentially Private Rewritten Text

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The study of Differential Privacy (DP) in Natural Language Processing often views the task of text privatization as a rewriting task, in which sensitive input texts are rewritten to hide explicit or implicit private information. In order to evaluate the privacy-preserving capabilities of a DP text rewriting mechanism, empirical privacy tests are frequently employed. In these tests, an adversary is modeled, who aims to infer sensitive information (e.g., gender) about the author behind a (privatized) text. Looking to improve the empirical protections provided by DP rewriting methods, we propose a simple post-processing method based on the goal of aligning rewritten texts with their original counterparts, where DP rewritten texts are rewritten again. Our results show that such an approach not only produces outputs that are more semantically reminiscent of the original inputs, but also texts which score on average better in empirical privacy evaluations. Therefore, our approach raises the bar for DP rewriting methods in their empirical privacy evaluations, providing an extra layer of protection against malicious adversaries.

Original languageEnglish
Title of host publicationARES 2024 - 19th International Conference on Availability, Reliability and Security, Proceedings
PublisherAssociation for Computing Machinery
ISBN (Electronic)9798400717185
DOIs
StatePublished - 30 Jul 2024
Event19th International Conference on Availability, Reliability and Security, ARES 2024 - Vienna, Austria
Duration: 30 Jul 20242 Aug 2024

Publication series

NameACM International Conference Proceeding Series

Conference

Conference19th International Conference on Availability, Reliability and Security, ARES 2024
Country/TerritoryAustria
CityVienna
Period30/07/242/08/24

Keywords

  • Data Privacy
  • Differential Privacy
  • Natural Language Processing

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