Aggregation-Aware Synthetic Text Generation Against Authorship Re-Identification
Quick summary
arXiv:2608.22161v1 Announce Type: new 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 ve
Key takeaways
- arXiv:2608.22161v1 Announce Type: new 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.
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “Aggregation-Aware Synthetic Text Generation Against Authorship Re-Identification” may reshape data collection, model training, output accountability and market access.
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