@inproceedings{kandula-etal-2024-improving,
title = "Improving Authorship Privacy: Adaptive Obfuscation with the Dynamic Selection of Techniques",
author = "Kandula, Hemanth and
Karakos, Damianos and
Qiu, Haoling and
Ulicny, Brian",
editor = "Habernal, Ivan and
Ghanavati, Sepideh and
Ravichander, Abhilasha and
Jain, Vijayanta and
Thaine, Patricia and
Igamberdiev, Timour and
Mireshghallah, Niloofar and
Feyisetan, Oluwaseyi",
booktitle = "Proceedings of the Fifth Workshop on Privacy in Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.privatenlp-1.14",
pages = "137--142",
abstract = "Authorship obfuscation, the task of rewriting text to protect the original author{'}s identity, is becoming increasingly important due to the rise of advanced NLP tools for authorship attribution techniques. Traditional methods for authorship obfuscation face significant challenges in balancing content preservation, fluency, and style concealment. This paper introduces a novel approach, the Obfuscation Strategy Optimizer (OSO), which dynamically selects the optimal obfuscation technique based on a combination of metrics including embedding distance, meaning similarity, and fluency. By leveraging an ensemble of language models OSO achieves superior performance in preserving the original content{'}s meaning and grammatical fluency while effectively concealing the author{'}s unique writing style. Experimental results demonstrate that the OSO outperforms existing methods and approaches the performance of larger language models. Our evaluation framework incorporates adversarial testing against state-of-the-art attribution systems to validate the robustness of the obfuscation techniques. We release our code publicly at https://github.com/BBN-E/ObfuscationStrategyOptimizer",
}
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<abstract>Authorship obfuscation, the task of rewriting text to protect the original author’s identity, is becoming increasingly important due to the rise of advanced NLP tools for authorship attribution techniques. Traditional methods for authorship obfuscation face significant challenges in balancing content preservation, fluency, and style concealment. This paper introduces a novel approach, the Obfuscation Strategy Optimizer (OSO), which dynamically selects the optimal obfuscation technique based on a combination of metrics including embedding distance, meaning similarity, and fluency. By leveraging an ensemble of language models OSO achieves superior performance in preserving the original content’s meaning and grammatical fluency while effectively concealing the author’s unique writing style. Experimental results demonstrate that the OSO outperforms existing methods and approaches the performance of larger language models. Our evaluation framework incorporates adversarial testing against state-of-the-art attribution systems to validate the robustness of the obfuscation techniques. We release our code publicly at https://github.com/BBN-E/ObfuscationStrategyOptimizer</abstract>
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%0 Conference Proceedings
%T Improving Authorship Privacy: Adaptive Obfuscation with the Dynamic Selection of Techniques
%A Kandula, Hemanth
%A Karakos, Damianos
%A Qiu, Haoling
%A Ulicny, Brian
%Y Habernal, Ivan
%Y Ghanavati, Sepideh
%Y Ravichander, Abhilasha
%Y Jain, Vijayanta
%Y Thaine, Patricia
%Y Igamberdiev, Timour
%Y Mireshghallah, Niloofar
%Y Feyisetan, Oluwaseyi
%S Proceedings of the Fifth Workshop on Privacy in Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F kandula-etal-2024-improving
%X Authorship obfuscation, the task of rewriting text to protect the original author’s identity, is becoming increasingly important due to the rise of advanced NLP tools for authorship attribution techniques. Traditional methods for authorship obfuscation face significant challenges in balancing content preservation, fluency, and style concealment. This paper introduces a novel approach, the Obfuscation Strategy Optimizer (OSO), which dynamically selects the optimal obfuscation technique based on a combination of metrics including embedding distance, meaning similarity, and fluency. By leveraging an ensemble of language models OSO achieves superior performance in preserving the original content’s meaning and grammatical fluency while effectively concealing the author’s unique writing style. Experimental results demonstrate that the OSO outperforms existing methods and approaches the performance of larger language models. Our evaluation framework incorporates adversarial testing against state-of-the-art attribution systems to validate the robustness of the obfuscation techniques. We release our code publicly at https://github.com/BBN-E/ObfuscationStrategyOptimizer
%U https://aclanthology.org/2024.privatenlp-1.14
%P 137-142
Markdown (Informal)
[Improving Authorship Privacy: Adaptive Obfuscation with the Dynamic Selection of Techniques](https://aclanthology.org/2024.privatenlp-1.14) (Kandula et al., PrivateNLP-WS 2024)
ACL