scholarly journals Privacy Preserving RBF Kernel Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Haoran Li ◽  
Li Xiong ◽  
Lucila Ohno-Machado ◽  
Xiaoqian Jiang
Keyword(s):  
Support Vector Machine ◽  
Performance Metrics ◽  
Differential Privacy ◽  
Privacy Preserving ◽  
Healthcare Research ◽  
Support Vector ◽  
Biomedical Data ◽  
Private Data ◽  
Public Data ◽  
Rbf Kernel

Data sharing is challenging but important for healthcare research. Methods for privacy-preserving data dissemination based on the rigorous differential privacy standard have been developed but they did not consider the characteristics of biomedical data and make full use of the available information. This often results in too much noise in the final outputs. We hypothesized that this situation can be alleviated by leveraging a small portion of open-consented data to improve utility without sacrificing privacy. We developed a hybrid privacy-preserving differentially private support vector machine (SVM) model that uses public data and private data together. Our model leverages the RBF kernel and can handle nonlinearly separable cases. Experiments showed that this approach outperforms two baselines: (1) SVMs that only use public data, and (2) differentially private SVMs that are built from private data. Our method demonstrated very close performance metrics compared to nonprivate SVMs trained on the private data.

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.

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

Interpreting Brain Waves

2017 ◽  
pp. 695-714
Author(s):  
Noran Magdy El-Kafrawy ◽  
Doaa Hegazy ◽  
Mohamed F. Tolba
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Basis Function ◽  
Brain Computer Interface ◽  
Support Vector ◽  
Left Hand ◽  
Brain Waves ◽  
Mode Decomposition ◽  
Rbf Kernel ◽  
The Brain

BCI (Brain-Computer Interface) gives you the power to manipulate things around you just by thinking of what you want to do. It allows your thoughts to be interpreted by the computer and hence act upon it. This could be utilized in helping disabled people, remote controlling of robots or even getting personalized systems depending upon your mood. The most important part of any BCI application is interpreting the brain signalsasthere are many mental tasks to be considered. In this chapter, the authors focus on interpreting motor imagery tasks and more specifically, imagining left hand, right hand, foot and tongue. Interpreting the signal consists of two main steps: feature extraction and classification. For the feature extraction,Empirical Mode Decomposition (EMD) was used and for the classification,the Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel was used. The authors evaluated this system using the BCI competition IV dataset and reached a very promising accuracy.

Research on Indicator Diagram of Shock Absorber Fault Recognition Based on Support Vector Machine

2013 ◽  
Vol 765-767 ◽  
pp. 2195-2198
Author(s):  
Wei Dong Xie ◽  
Kan Gao ◽  
Ji Sheng Shen
Keyword(s):  
Support Vector Machine ◽  
Shock Absorber ◽  
Recognition Rate ◽  
Support Vector ◽  
Indicator Diagram ◽  
Fault Recognition ◽  
Average Recognition Rate ◽  
Rbf Kernel ◽  
On Line ◽  
Rbf Kernel Function

In order to meet the development of shock absorber on-line detection, a new method of indicator diagrams recognition for shock absorber based on support vector machine (SVM) is proposed. Different fault patterns of shock absorber indicator diagram are discussed, including their main causes. The recognition model is constructed each with Linear, Polynomial and Radial Basis Function (RBF) kernel function. The experimental results show that the best average recognition rate is 96.4%. This method is effective in indicator diagram fault recognition of shock absorber.

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

scholarly journals Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 667
Author(s):  
Wismaji Sadewo ◽  
Zuherman Rustam ◽  
Hamidah Hamidah ◽  
Alifah Roudhoh Chusmarsyah
Keyword(s):  
Machine Learning ◽  
Pancreatic Cancer ◽  
Support Vector Machine ◽  
Early Detection ◽  
Feature Space ◽  
Kernel Functions ◽  
Twin Support Vector Machine ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Rbf Kernel

Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s.

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