# SensitiveNets: Learning Agnostic Representations with Application to Face Images

arXiv: https://arxiv.org/abs/1902.00334 · HTML: https://mlsanity.com/paper/1902.00334/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2019.

## Built on by (most cited first, 18 of 18)

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- [IFBiD: Inference-Free Bias Detection](https://mlsanity.com/paper/2109.04374/) (2021)
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- [Toward Face Biometric De-identification using Adversarial Examples](https://mlsanity.com/paper/2302.03657/) (2023)
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- [Membership Inference Test: Auditing Training Data in Object Classification Models](https://mlsanity.com/paper/2601.12929/) (2026)
- [Unraveling Machine Behavior by Multi-Level Bias Analysis and Detection: Methodology and Application to Computer Vision](https://mlsanity.com/paper/2607.07236/) (2026)
- [SaFL: Sybil-aware Federated Learning with Application to Face Recognition](https://mlsanity.com/paper/2311.04346/) (2023)
- [Comprehensive Equity Index (CEI): Definition and Application to Bias Evaluation in Biometrics](https://mlsanity.com/paper/2409.01928/) (2024)
- [Investigating Fairness of Ocular Biometrics Among Young, Middle-Aged, and Older Adults](https://mlsanity.com/paper/2110.01641/) (2021)
- [Blockchain and Biometrics: Survey, GDPR Analysis, and Future Directions](https://mlsanity.com/paper/2302.10883/) (2023)
- [inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs](https://mlsanity.com/paper/2610.18051/) (2026)

## Abstract (arXiv)

This work proposes a novel privacy-preserving neural network feature representation to suppress the sensitive information of a learned space while maintaining the utility of the data. The new international regulation for personal data protection forces data controllers to guarantee privacy and avoid discriminative hazards while managing sensitive data of users. In our approach, privacy and discrimination are related to each other. Instead of existing approaches aimed directly at fairness improvement, the proposed feature representation enforces the privacy of selected attributes. This way fairness is not the objective, but the result of a privacy-preserving learning method. This approach guarantees that sensitive information cannot be exploited by any agent who process the output of the model, ensuring both privacy and equality of opportunity. Our method is based on an adversarial regularizer that introduces a sensitive information removal function in the learning objective. The method is evaluated on three different primary tasks (identity, attractiveness, and smiling) and three publicly available benchmarks. In addition, we present a new face annotation dataset with balanced distribution between genders and ethnic origins. The experiments demonstrate that it is possible to improve the privacy and equality of opportunity while retaining competitive performance independently of the task.