scholarly journals Privacy-Preserving Multi-Class Support Vector Machine for Outsourcing the Data Classification in Cloud

2014 ◽  
Vol 11 (5) ◽  
pp. 467-479 ◽  
Author(s):  
Yogachandran Rahulamathavan ◽  
Raphael C.-W. Phan ◽  
Suresh Veluru ◽  
Kanapathippillai Cumanan ◽  
Muttukrishnan Rajarajan
Keyword(s):  
Support Vector Machine ◽  
Data Classification ◽  
Privacy Preserving ◽  
Support Vector

Imbalanced data classification algorithm with support vector machine kernel extensions

2018 ◽  
Vol 12 (3) ◽  
pp. 341-347 ◽  
Author(s):  
Feng Wang ◽  
Shaojiang Liu ◽  
Weichuan Ni ◽  
Zhiming Xu ◽  
Zemin Qiu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Imbalanced Data ◽  
Data Classification ◽  
Classification Algorithm ◽  
Support Vector ◽  
Imbalanced Data Classification

An efficient weighted Lagrangian twin support vector machine for imbalanced data classification

2014 ◽  
Vol 47 (9) ◽  
pp. 3158-3167 ◽  
Author(s):  
Yuan-Hai Shao ◽  
Wei-Jie Chen ◽  
Jing-Jing Zhang ◽  
Zhen Wang ◽  
Nai-Yang Deng
Keyword(s):  
Support Vector Machine ◽  
Imbalanced Data ◽  
Data Classification ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Imbalanced Data Classification

scholarly journals Privacy-Preserving Outsourced Support Vector Machine Design for Secure Drug Discovery

2020 ◽  
Vol 8 (2) ◽  
pp. 610-622 ◽  
Author(s):  
Ximeng Liu ◽  
Robert H. Deng ◽  
Kim-Kwang Raymond Choo ◽  
Yang Yang
Keyword(s):  
Support Vector Machine ◽  
Drug Discovery ◽  
Privacy Preserving ◽  
Machine Design ◽  
Support Vector

scholarly journals Differentially Private Image Classification Using Support Vector Machine and Differential Privacy

2019 ◽  
Vol 1 (1) ◽  
pp. 483-491 ◽  
Author(s):  
Makhamisa Senekane
Keyword(s):  
Support Vector Machine ◽  
Data Analysis ◽  
Image Classification ◽  
Differential Privacy ◽  
Privacy Preserving ◽  
Global Optimum ◽  
Support Vector ◽  
Sensitive Data ◽  
Radiological Images ◽  
Golden Standard

The ubiquity of data, including multi-media data such as images, enables easy mining and analysis of such data. However, such an analysis might involve the use of sensitive data such as medical records (including radiological images) and financial records. Privacy-preserving machine learning is an approach that is aimed at the analysis of such data in such a way that privacy is not compromised. There are various privacy-preserving data analysis approaches such as k-anonymity, l-diversity, t-closeness and Differential Privacy (DP). Currently, DP is a golden standard of privacy-preserving data analysis due to its robustness against background knowledge attacks. In this paper, we report a scheme for privacy-preserving image classification using Support Vector Machine (SVM) and DP. SVM is chosen as a classification algorithm because unlike variants of artificial neural networks, it converges to a global optimum. SVM kernels used are linear and Radial Basis Function (RBF), while ϵ -differential privacy was the DP framework used. The proposed scheme achieved an accuracy of up to 98%. The results obtained underline the utility of using SVM and DP for privacy-preserving image classification.

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