# Statistical Privacy Guarantees of Machine Learning Preprocessing Techniques

arXiv: https://arxiv.org/abs/2109.02496 · HTML: https://mlsanity.com/paper/2109.02496/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Auditing Differentially Private Machine Learning: How Private is Private SGD?](https://mlsanity.com/paper/2006.07709/) (2020)
- [Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning](https://mlsanity.com/paper/2101.04535/) (2021)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Differential Privacy and Machine Learning: a Survey and Review](https://mlsanity.com/paper/1412.7584/) (2014)
- [Diffprivlib: The IBM Differential Privacy Library](https://mlsanity.com/paper/1907.02444/) (2019)
- [Detecting Violations of Differential Privacy](https://mlsanity.com/paper/1805.10277/) (2018)
- [Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning](https://mlsanity.com/paper/1805.05838/) (2018)
- [Private Approximations of the 2nd-Moment Matrix Using Existing Techniques in Linear Regression](https://mlsanity.com/paper/1507.00056/) (2015)

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

- [Differential Privacy Under Class Imbalance: Methods and Empirical Insights](https://mlsanity.com/paper/2411.05733/) (2024)

## Abstract (arXiv)

Differential privacy provides strong privacy guarantees for machine learning applications. Much recent work has been focused on developing differentially private models, however there has been a gap in other stages of the machine learning pipeline, in particular during the preprocessing phase. Our contributions are twofold: we adapt a privacy violation detection framework based on statistical methods to empirically measure privacy levels of machine learning pipelines, and apply the newly created framework to show that resampling techniques used when dealing with imbalanced datasets cause the resultant model to leak more privacy. These results highlight the need for developing private preprocessing techniques.