# Interpretable Privacy Preservation of Text Representations Using Vector Steganography

arXiv: https://arxiv.org/abs/2112.02557 · HTML: https://mlsanity.com/paper/2112.02557/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 5 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Privacy-preserving Neural Representations of Text](https://mlsanity.com/paper/1808.09408/) (2018)
- [TextHide: Tackling Data Privacy in Language Understanding Tasks](https://mlsanity.com/paper/2010.06053/) (2020)
- [Low Anisotropy Sense Retrofitting (LASeR) : Towards Isotropic and Sense Enriched Representations](https://mlsanity.com/paper/2104.10833/) (2021)
- [Model Choices Influence Attributive Word Associations: A Semi-supervised Analysis of Static Word Embeddings](https://mlsanity.com/paper/2012.07978/) (2020)

## Abstract (arXiv)

Contextual word representations generated by language models (LMs) learn spurious associations present in the training corpora. Recent findings reveal that adversaries can exploit these associations to reverse-engineer the private attributes of entities mentioned within the corpora. These findings have led to efforts towards minimizing the privacy risks of language models. However, existing approaches lack interpretability, compromise on data utility and fail to provide privacy guarantees. Thus, the goal of my doctoral research is to develop interpretable approaches towards privacy preservation of text representations that retain data utility while guaranteeing privacy. To this end, I aim to study and develop methods to incorporate steganographic modifications within the vector geometry to obfuscate underlying spurious associations and preserve the distributional semantic properties learnt during training.