Privacy-Preserving Gradient-Descent Methods

2010 ◽  
Vol 22 (6) ◽  
pp. 884-899 ◽  
Author(s):  
Shuguo Han ◽  
Wee Keong Ng ◽  
Li Wan ◽  
Vincent C.S. Lee
Keyword(s):  
Gradient Descent ◽  
Privacy Preserving ◽  
Descent Methods ◽  
Gradient Descent Methods

scholarly journals Distributed Outsourced Privacy-Preserving Gradient Descent Methods among Multiple Parties

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zuowen Tan ◽  
Haohan Zhang ◽  
Peiyi Hu ◽  
Rui Gao
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Privacy ◽  
Gradient Descent ◽  
High Efficiency ◽  
Target Function ◽  
Privacy Preserving ◽  
Descent Methods ◽  
Gradient Descent Methods ◽  
Multiple Data

The Internet of Things (IoT) is one of the latest internet evolutions. Cloud computing is an important technique which realizes the computational demand of largely distributed IoT devices/sensors by employing various machine learning models. Gradient descent methods are widely employed to find the optimal coefficients of a machine learning model in the cloud computing. Commonly, the data are distributed among multiple data owners, whereas the target function is held by the model owner. The model owner can train its model over data owner’s data and provide predictions. However, the dataset or the target function’s confidentiality may not be kept in secret during computations. Thus, security threats and privacy risks arise. To address the data and model’s privacy mentioned above, we present two new outsourced privacy-preserving gradient descent (OPPGD) method schemes over horizontally or vertically partitioned data among multiple parties, respectively. Compared to previously proposed solutions, our methods improve in comprehensiveness in a more general scene. The data privacy and the model privacy are preserved during the whole learning and prediction procedures. In addition, the execution performance evaluation demonstrates that our schemes can help the model owner to optimize its target function and provide exact prediction with high efficiency and accuracy.

Multiobjective Sequence Design via Gradient Descent Methods

2018 ◽  
Vol 54 (3) ◽  
pp. 1237-1252 ◽  
Author(s):  
John Michael Baden ◽  
Brian O'Donnell ◽  
Lance Schmieder
Keyword(s):  
Gradient Descent ◽  
Descent Methods ◽  
Sequence Design ◽  
Gradient Descent Methods
Sign in / Sign up

Export Citation Format

Share Document