Learning algorithms for vector quantization using vertically partitioned data with IoT

2021 ◽  
Author(s):  
Hirofumi Miyajima ◽  
Noritaka Shigei ◽  
Hiromi Miyajima ◽  
Norio Shiratori
Keyword(s):  
Vector Quantization ◽  
Learning Algorithms ◽  
Partitioned Data ◽  
Vertically Partitioned Data

scholarly journals Partition Based Perturbation for Privacy Preserving Distributed Data Mining

2017 ◽  
Vol 17 (2) ◽  
pp. 44-55 ◽  
Author(s):  
M. Antony Sheela ◽  
K. Vijayalakshmi
Keyword(s):  
Data Mining ◽  
Threshold Level ◽  
Third Party ◽  
Distributed Data Mining ◽  
Distributed Data ◽  
Data Perturbation ◽  
Private Data ◽  
Partitioned Data ◽  
Vertically Partitioned Data ◽  
The Mean

Abstract Data mining on vertically or horizontally partitioned dataset has the overhead of protecting the private data. Perturbation is a technique that protects the revealing of data. This paper proposes a perturbation and anonymization technique that is performed on the vertically partitioned data. A third-party coordinator is used to partition the data recursively in various parties. The parties perturb the data by finding the mean, when the specified threshold level is reached. The perturbation maintains the statistical relationship among attributes.

Privacy-preserving decision trees over vertically partitioned data

2008 ◽  
Vol 2 (3) ◽  
pp. 1-27 ◽  
Author(s):  
Jaideep Vaidya ◽  
Chris Clifton ◽  
Murat Kantarcioglu ◽  
A. Scott Patterson
Keyword(s):  
Decision Trees ◽  
Privacy Preserving ◽  
Partitioned Data ◽  
Vertically Partitioned Data
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