2 research outputs found
Privacy-Preserving Chaotic Extreme Learning Machine with Fully Homomorphic Encryption
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.Comment: 26 pages; 1 Figure; 7 Tables. arXiv admin note: text overlap with
arXiv:2205.1326
Privacy-Preserving Wavelet Neural Network with Fully Homomorphic Encryption
The main aim of Privacy-Preserving Machine Learning (PPML) is to protect the
privacy and provide security to the data used in building Machine Learning
models. There are various techniques in PPML such as Secure Multi-Party
Computation, Differential Privacy, and Homomorphic Encryption (HE). The
techniques are combined with various Machine Learning models and even Deep
Learning Networks to protect the data privacy as well as the identity of the
user. In this paper, we propose a fully homomorphic encrypted wavelet neural
network to protect privacy and at the same time not compromise on the
efficiency of the model. We tested the effectiveness of the proposed method on
seven datasets taken from the finance and healthcare domains. The results show
that our proposed model performs similarly to the unencrypted model.Comment: 17 pages; 3 figures, 10 table