Aggregation-Aware Synthetic Text Generation Against Authorship Re-Identification

Abstract:Online users often release multiple texts under the same identity, giving attackers an author profile that can reveal more than any single text. Existing authorship obfuscation methods optimize privacy independently for each document, leaving them blind to cross-document correlations that make aggregation dangerous. We propose Aggregation-Aware Synthetic Text Generation (AAST), a framework that addresses this gap by jointly selecting synthetic texts at the bundle level rather than optimizing each text in isolation. AAST targets attribution and verification attacks, including cross-genre settings where attacker references come from a genre not observed during generation or selection. Experiments across same-genre, cross-genre, neural, and independent non-neural stylometric attacks show that AAST lowers account-level linkability as bundle size grows, while preserving semantic quality, linguistic acceptability, and sentiment alignment.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2608.22161 [cs.AI]

(or arXiv:2608.22161v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.22161 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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