  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.
