# Privacy-Preserving Chaotic Extreme Learning Machine with Fully Homomorphic Encryption

arXiv: https://arxiv.org/abs/2208.02587 · HTML: https://mlsanity.com/paper/2208.02587/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [Privacy-Preserving Machine Learning: Methods, Challenges and Directions](https://mlsanity.com/paper/2108.04417/) (2021)
- [Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network](https://mlsanity.com/paper/2106.07229/) (2021)
- [Crypto-Nets: Neural Networks over Encrypted Data](https://mlsanity.com/paper/1412.6181/) (2014)
- [Secure Multilayer Perceptron Based On Homomorphic Encryption](https://mlsanity.com/paper/1806.02709/) (2018)
- [Conditionals in Homomorphic Encryption and Machine Learning Applications](https://mlsanity.com/paper/1810.12380/) (2018)

## Abstract (arXiv)

The Machine Learning and Deep Learning Models require a lot of data for the training process, and in some scenarios, there might be some sensitive data, such as customer information involved, which the organizations might be hesitant to outsource for model building. Some of the privacy-preserving techniques such as Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation can be integrated with different Machine Learning and Deep Learning algorithms to provide security to the data as well as the model. In this paper, we propose a Chaotic Extreme Learning Machine and its encrypted form using Fully Homomorphic Encryption where the weights and biases are generated using a logistic map instead of uniform distribution. Our proposed method has performed either better or similar to the Traditional Extreme Learning Machine on most of the datasets.