# Towards a Data Privacy-Predictive Performance Trade-off

arXiv: https://arxiv.org/abs/2201.05226 · HTML: https://mlsanity.com/paper/2201.05226/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 75 references that are in the map.
- First posted on arXiv 13 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis](https://mlsanity.com/paper/1606.04316/) (2016)
- [Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training](https://mlsanity.com/paper/2008.08384/) (2020)
- [Locality Sensitive Hashing with Extended Differential Privacy](https://mlsanity.com/paper/2010.09393/) (2020)

## Built on by (most cited first, 6 of 6)

- [A Survey on Privacy Risks and Protection in Large Language Models](https://mlsanity.com/paper/2505.01976/) (2025)
- [The Pitfalls of "Security by Obscurity" And What They Mean for Transparent AI](https://mlsanity.com/paper/2501.18669/) (2025)
- [Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models](https://mlsanity.com/paper/2502.21123/) (2025)
- [Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution](https://mlsanity.com/paper/2605.02640/) (2026)
- [Automated Privacy-Preserving Techniques via Meta-Learning](https://mlsanity.com/paper/2406.16456/) (2024)
- [Multi-View Graph Feature Propagation for Privacy Preservation and Feature Sparsity](https://mlsanity.com/paper/2510.11347/) (2025)

## Abstract (arXiv)

Machine learning is increasingly used in the most diverse applications and domains, whether in healthcare, to predict pathologies, or in the financial sector to detect fraud. One of the linchpins for efficiency and accuracy in machine learning is data utility. However, when it contains personal information, full access may be restricted due to laws and regulations aiming to protect individuals' privacy. Therefore, data owners must ensure that any data shared guarantees such privacy. Removal or transformation of private information (de-identification) are among the most common techniques. Intuitively, one can anticipate that reducing detail or distorting information would result in losses for model predictive performance. However, previous work concerning classification tasks using de-identified data generally demonstrates that predictive performance can be preserved in specific applications. In this paper, we aim to evaluate the existence of a trade-off between data privacy and predictive performance in classification tasks. We leverage a large set of privacy-preserving techniques and learning algorithms to provide an assessment of re-identification ability and the impact of transformed variants on predictive performance. Unlike previous literature, we confirm that the higher the level of privacy (lower re-identification risk), the higher the impact on predictive performance, pointing towards clear evidence of a trade-off.