scholarly journals Distributed Data Mining with Differential Privacy

2011 ◽  
Author(s):  
Ning Zhang ◽  
Ming Li ◽  
Wenjing Lou
Keyword(s):  
Data Mining ◽  
Differential Privacy ◽  
Distributed Data Mining ◽  
Distributed Data

scholarly journals A differentially private distributed data mining scheme with high efficiency for edge computing

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Xianwen Sun ◽  
Ruzhi Xu ◽  
Longfei Wu ◽  
Zhitao Guan
Keyword(s):  
Data Mining ◽  
Differential Privacy ◽  
Edge Computing ◽  
Distributed Data Mining ◽  
Good Prediction ◽  
Distributed Data ◽  
Good Prediction Accuracy ◽  
Wide Range ◽  
Ensemble Strategy ◽  
Mining Scheme

AbstractA wide range of data mining applications benefit from the low latency offered by edge computing. However, edge computing suffers from limited computing resources, which inhibits the applications of the computationally expensive data mining methods. In the edge-cloud environment, usually, the participants turn to collaboratively train machine-learning models that yield more accurate prediction results. However, data owners may not be willing to sharing the own data for the privacy concerns. To handle such disparate goals, we focus on tree-based distributed data mining scheme with differential privacy, which is computationally friendly. The basic idea of our approach is based on a distributed ensemble strategy. Each participant builds an elegant decision model based on their own data, which has a good tradeoff between the computation and the accuracy of the data distribution, and shares it with other participants after being injected with the elaborate noise. Then the useful knowledge transferred from the decision models is acquired by other participants in an adaptive ensemble strategy. Both the theoretical analysis and the experiments show that our scheme provides an efficient data mining manner that can achieve a good prediction accuracy while providing rigorous privacy guarantee over the distributed data.

A Windowing strategy for Distributed Data Mining optimized through GPUs

2017 ◽  
Vol 93 ◽  
pp. 23-30 ◽  
Author(s):  
Xavier Limón ◽  
Alejandro Guerra-Hernández ◽  
Nicandro Cruz-Ramírez ◽  
Héctor-Gabriel Acosta-Mesa ◽  
Francisco Grimaldo
Keyword(s):  
Data Mining ◽  
Distributed Data Mining ◽  
Distributed Data
Sign in / Sign up

Export Citation Format

Share Document