# Siamese Generative Adversarial Privatizer for Biometric Data

arXiv: https://arxiv.org/abs/1804.08757 · HTML: https://mlsanity.com/paper/1804.08757/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 5 of 5)

- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Anonymizing medical case-based explanations through disentanglement](https://mlsanity.com/paper/2311.04833/) (2023)
- [Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review](https://mlsanity.com/paper/2306.00687/) (2023)
- [Towards Privacy-preserving Explanations in Medical Image Analysis](https://mlsanity.com/paper/2107.09652/) (2021)
- [PRIVATEEDIT: A Privacy-Preserving Pipeline for Face-Centric Generative Image Editing](https://mlsanity.com/paper/2603.03412/) (2026)

## Abstract (arXiv)

State-of-the-art machine learning algorithms can be fooled by carefully crafted adversarial examples. As such, adversarial examples present a concrete problem in AI safety. In this work we turn the tables and ask the following question: can we harness the power of adversarial examples to prevent malicious adversaries from learning identifying information from data while allowing non-malicious entities to benefit from the utility of the same data? For instance, can we use adversarial examples to anonymize biometric dataset of faces while retaining usefulness of this data for other purposes, such as emotion recognition? To address this question, we propose a simple yet effective method, called Siamese Generative Adversarial Privatizer (SGAP), that exploits the properties of a Siamese neural network to find discriminative features that convey identifying information. When coupled with a generative model, our approach is able to correctly locate and disguise identifying information, while minimally reducing the utility of the privatized dataset. Extensive evaluation on a biometric dataset of fingerprints and cartoon faces confirms usefulness of our simple yet effective method.