Privacy Aware Non-linear Support Vector Machine for Multi-source Big Data

Lu, Yunmei; Phoungphol, Piyaphol; Zhang, Yanqing

doi:10.1109/trustcom.2014.103

Cited by 4 publications

(7 citation statements)

References 28 publications

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“…When applied to a classification problem, wrapper methods used for selecting features are closely related to the classifiers. In the current work, classification accuracy is used as the wrapper method, by which features will be evaluated at each iteration, and PAN-SVM [33] is used as the binary classifier. PAN-SVM contains three layers, where each layer performs corresponding functions.…”

Section: Pan-svm Classifiermentioning

confidence: 99%

“…Furthermore, the training process is speeded up when compared with other distributed classification methods. Details about PAN-SVM could be found in [33]. Step 1.…”

Section: Pan-svm Classifiermentioning

confidence: 99%

“…1. PPM2C is also based on [33]. PAN-SVM proposed a privacy preserving framework for binary classification based on horizontally located data.…”

Section: Privacy Perserving Multiclass Classificationmentioning

confidence: 99%

“…In this work, we are interested in the investigation of Privacy Preserving Feature Selection and Multi-Class Classification problems for horizontally distributed data. Based on one of our previous published paper [33], we propose a method of Privacy Preserving Feature Selection via Integrating Filter and Wrapper approaches for Horizontally Distributed Data, named PPFS-IFW and a privacy preserving multiclass classification algorithm named PPM2C. Experiments had been conducted to validate the performance of the proposed approaches.…”

mentioning

confidence: 99%

“…To solve the problem of privacy concern during feature selection, we propose a Privacy Preserving Feature Selection method via Integrating Filter and Wrapper approaches (named PPFS-IFW in brief) for Horizontally Distributed Data. PPFS-IFW is based on our previously published privacy preserving framework, which is appointed by Privacy Aware Non-linear SVM for Multi-source Big Data (PAN-SVM) [33].…”

mentioning

confidence: 99%

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Privacy preserving feature selection and Multiclass Classification for horizontally distributed data

Lu¹,

Yan²,

Han³

et al. 2018

Mathematical Foundations of Computing

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In the last two decades, a lot of scientific fields have experienced a huge growth in data volume and data complexity, which brings data miners lots of opportunities, as well as many challenges. With the advent of the era of big data, applying data mining techniques on assembling data from multiple parties (or sources) has become a leading trend. However, those data mining tasks may divulge individuals' privacy, which leads to the increased concerns in privacy preserving. In this work, a Privacy Preserving feature selection method (PPFS-IFW) and Multiclass Classification method (PPM2C) are proposed. Experiments had been conducted to validate the performance of the proposed approaches. Both PPFS-IFW and PPM2C were tested on six benchmark datasets. The testing results demonstrate PPFS-IFW's capability in enhancing the classification performance at the level of accuracy by selection informative features. PPFS-IFW can not only preserve private information but also outperform some other state-of-the-art feature selection approaches. Experimental results also show that the proposed PPM2C method is workable and stable. Particularly, It reduces the risk of over-fitting when compared with the regular Support Vector Machine. In the meantime, by employing the Secure Sum Protocol to encrypt data at the bottom layer, users' privacy is preserved.

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Section: Pan-svm Classifiermentioning

confidence: 99%

“…Furthermore, the training process is speeded up when compared with other distributed classification methods. Details about PAN-SVM could be found in [33]. Step 1.…”

Section: Pan-svm Classifiermentioning

confidence: 99%

“…1. PPM2C is also based on [33]. PAN-SVM proposed a privacy preserving framework for binary classification based on horizontally located data.…”

Section: Privacy Perserving Multiclass Classificationmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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