# Defending Against Authorship Identification Attacks

arXiv: https://arxiv.org/abs/2310.01568 · HTML: https://mlsanity.com/paper/2310.01568/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 128 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Builds on (most cited first, 30 of 34)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Deep Learning for Text Style Transfer: A Survey](https://mlsanity.com/paper/2011.00416/) (2020)
- [Counter-fitting Word Vectors to Linguistic Constraints](https://mlsanity.com/paper/1603.00892/) (2016)
- [CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks](https://mlsanity.com/paper/2005.09544/) (2020)
- [White-Box Transformers via Sparse Rate Reduction](https://mlsanity.com/paper/2306.01129/) (2023)
- [The Limits of Word Level Differential Privacy](https://mlsanity.com/paper/2205.02130/) (2022)
- [A4NT: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation](https://mlsanity.com/paper/1711.01921/) (2017)
- [Speaker Anonymization Using X-vector and Neural Waveform Models](https://mlsanity.com/paper/1905.13561/) (2019)
- [ER-AE: Differentially Private Text Generation for Authorship Anonymization](https://mlsanity.com/paper/1907.08736/) (2019)
- [DP-BART for Privatized Text Rewriting under Local Differential Privacy](https://mlsanity.com/paper/2302.07636/) (2023)
- [Evaluating prose style transfer with the Bible](https://mlsanity.com/paper/1711.04731/) (2017)
- [Privacy-Preserving Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/2102.09604/) (2021)
- [Idiosyncratic but not Arbitrary: Learning Idiolects in Online Registers Reveals Distinctive yet Consistent Individual Styles](https://mlsanity.com/paper/2109.03158/) (2021)
- [Text Revision by On-the-Fly Representation Optimization](https://mlsanity.com/paper/2204.07359/) (2022)
- [The Case for Being Average: A Mediocrity Approach to Style Masking and Author Obfuscation](https://mlsanity.com/paper/1707.03736/) (2017)
- [Counterfactual Evaluation for Explainable AI](https://mlsanity.com/paper/2109.01962/) (2021)
- [Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators](https://mlsanity.com/paper/2203.09813/) (2022)
- [Style Obfuscation by Invariance](https://mlsanity.com/paper/1805.07143/) (2018)
- [DeepStyle: User Style Embedding for Authorship Attribution of Short Texts](https://mlsanity.com/paper/2103.11798/) (2021)
- [Fighting Authorship Linkability with Crowdsourcing](https://mlsanity.com/paper/1405.4918/) (2014)
- [Avengers Ensemble! Improving Transferability of Authorship Obfuscation](https://mlsanity.com/paper/2109.07028/) (2021)
- [Author Obfuscation Using Generalised Differential Privacy](https://mlsanity.com/paper/1805.08866/) (2018)

## Built on by (most cited first, 1 of 1)

- [Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation](https://mlsanity.com/paper/2403.11265/) (2024)

## Abstract (arXiv)

Authorship identification has proven unsettlingly effective in inferring the identity of the author of an unsigned document, even when sensitive personal information has been carefully omitted. In the digital era, individuals leave a lasting digital footprint through their written content, whether it is posted on social media, stored on their employer's computers, or located elsewhere. When individuals need to communicate publicly yet wish to remain anonymous, there is little available to protect them from unwanted authorship identification. This unprecedented threat to privacy is evident in scenarios such as whistle-blowing. Proposed defenses against authorship identification attacks primarily aim to obfuscate one's writing style, thereby making it unlinkable to their pre-existing writing, while concurrently preserving the original meaning and grammatical integrity. The presented work offers a comprehensive review of the advancements in this research area spanning over the past two decades and beyond. It emphasizes the methodological frameworks of modification and generation-based strategies devised to evade authorship identification attacks, highlighting joint efforts from the differential privacy community. Limitations of current research are discussed, with a spotlight on open challenges and potential research avenues.