# Privacy-preserving Machine Learning for Medical Image Classification

arXiv: https://arxiv.org/abs/2108.12816 · HTML: https://mlsanity.com/paper/2108.12816/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 29 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds](https://mlsanity.com/paper/1605.02065/) (2016)
- [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018)
- [Gazelle: A Low Latency Framework for Secure Neural Network Inference](https://mlsanity.com/paper/1801.05507/) (2018)
- [Concentrated Differential Privacy](https://mlsanity.com/paper/1603.01887/) (2016)
- [A General Approach to Adding Differential Privacy to Iterative Training Procedures](https://mlsanity.com/paper/1812.06210/) (2018)
- [Private Machine Learning in TensorFlow using Secure Computation](https://mlsanity.com/paper/1810.08130/) (2018)

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

- [Privacy-preserving machine learning for healthcare: open challenges and future perspectives](https://mlsanity.com/paper/2303.15563/) (2023)

## Abstract (arXiv)

With the rising use of Machine Learning (ML) and Deep Learning (DL) in various industries, the medical industry is also not far behind. A very simple yet extremely important use case of ML in this industry is for image classification. This is important for doctors to help them detect certain diseases timely, thereby acting as an aid to reduce chances of human judgement error. However, when using automated systems like these, there is a privacy concern as well. Attackers should not be able to get access to the medical records and images of the patients. It is also required that the model be secure, and that the data that is sent to the model and the predictions that are received both should not be revealed to the model in clear text. In this study, we aim to solve these problems in the context of a medical image classification problem of detection of pneumonia by examining chest x-ray images.