Improving Authorship Privacy: Adaptive Obfuscation with the Dynamic Selection of Techniques (2024)

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

## introduction the digital age has brought about profound changes in how information is created, shared, and analyzed. one critical aspect of this transformation is the increasing capability to attribute texts to their authors using powerful authorship attribution systems by analyzing text style alone @xcite @xcite . these create both opportunities and challenges, particularly when they intersect with issues of privacy and anonymity. authorship obfuscation seeks to address these challenges by modifying a text's stylistic features to prevent the identification of its author. the need for such measures spans various domains, from protecting journalists and political dissidents against persecution to preserving anonymity in peer review processes. the primary goal is to protect the public from potential abuses of authorship attribution techniques, which could stifle free speech or target whistleblowers. authorship obfuscation involves strategically altering writing style to obscure stylistic signatures that might trace the text back to its author, thereby protecting their identity @xcite . the challenge lies in concealing the author's style without compromising the text's content integrity. current approaches to authorship obfuscation vary widely, from using large language models (llms) like chatgpt, which, while powerful, require substantial computational resources and potentially compromise privacy if proprietary data retention is involved. on the other end of the spectrum are more localized, machine translation system @xcite , rule-based systems @xcite or iterative-change algorithms @xcite that often struggle with the dual demands of effective obfuscation and content preservation. more recently @xcite , on the other hand, proposed an inferencetime algorithm that utilizes constrained decoding for author anonymity, providing flexibility and userspecified control. @xcite proposed a style transfer method that effectively adjusts styles from arbitrary sources to target styles while preserving content. each exhibits diverse strengths and weaknesses due to variations in data, architectures, and hyperparameters, making them complementary to each other. therefore, it is important to dynamically ensemble these systems to generate consistently better obfuscation for each input. considering the diverse strengths and weaknesses of these methods, it is crucial to develop an ensembling method that harnesses their complementary potentials. we introduce the obfuscation strategy optimizer (oso), an ensemble-based approach de-signed to dynamically select the optimal obfuscation strategy that aligns with users' needs. users will be able to leverage oso over outputs from different kinds of obfuscation systems to optimize the trade-off between style concealment and content preservation while preserving the semantic integrity and readability of the original text. oso can operate over many other obfuscation systems and align with new users' needs with very small configuration efforts. 2 approach: obfuscation selection for ## experiments & system evaluation we conducted experiments to evaluate the performance of the oso compared to individual obfuscation methods. these experiments were designed to measure the effectiveness of oso in preserving content, ensuring fluency, and achieving style concealment. we used a diverse dataset comprising texts from multiple authors to assess the oso's generalizability. we also compared it with existing baseline authorship obfuscation methods such as mutant-x @xcite and jamdec @xcite . ## conclusion in this work, we proposed a novel obfuscation strategy optimizer (oso) to improve authorship obfuscation. by leveraging multiple obfuscation techniques and dynamically selecting the most effective one based on a set of well-defined metrics, the oso offers a robust and flexible solution to protect authorship privacy. our experimental results highlight the efficacy of the oso in maintaining content integrity and fluency while effectively obfuscating the author's style. future work will involve expanding the oso with additional obfuscation techniques and further refining the algorithm. we aim to explore more scalable optimization methods, such as heuristic searches and reinforcement learning-based strategies, to improve the oso's performance and efficiency.