  Information Disguise (ID), a part of computational ethics in Natural Language
Processing (NLP), is concerned with best practices of textual paraphrasing to
prevent the non-consensual use of authors' posts on the Internet. Research on
ID becomes important when authors' written online communication pertains to
sensitive domains, e.g., mental health. Over time, researchers have utilized
AI-based automated word spinners (e.g., SpinRewriter, WordAI) for paraphrasing
content. However, these tools fail to satisfy the purpose of ID as their
paraphrased content still leads to the source when queried on search engines.
There is limited prior work on judging the effectiveness of paraphrasing
methods for ID on search engines or their proxies, neural retriever (NeurIR)
models. We propose a framework where, for a given sentence from an author's
post, we perform iterative perturbation on the sentence in the direction of
paraphrasing with an attempt to confuse the search mechanism of a NeurIR system
when the sentence is queried on it. Our experiments involve the subreddit
'r/AmItheAsshole' as the source of public content and Dense Passage Retriever
as a NeurIR system-based proxy for search engines. Our work introduces a novel
method of phrase-importance rankings using perplexity scores and involves
multi-level phrase substitutions via beam search. Our multi-phrase substitution
scheme succeeds in disguising sentences 82% of the time and hence takes an
essential step towards enabling researchers to disguise sensitive content
effectively before making it public. We also release the code of our approach.
