A Novel Anti-classification Approach for Knowledge Protection.
Classification is the problem of identifying a set of categories where new data belong, on the basis of a set of training data whose category membership is known. Its application is wide-spread, such as the medical science domain. The issue of the classification knowledge protection has been paid at...
| Publicado en: | Journal of Medical Systems Vol. 39; no. 10; pp. 1 - 11 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115925181&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925181 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2015 vid: 39 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925181 115925181 115925181 10.1007/s10916-015-0305-4 115925181 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: A Novel Anti-classification Approach for Knowledge Protection. aug: au: Lin, Chen-Yi Chen, Tung-Shou Tsai, Hui-Fang Lee, Wei-Bin Hsu, Tien-Yu Kao, Yuan-Hung affil: Department of Information Management, National Taichung University of Science and Technology, Taichung City Taiwan sug: subj: Classification Knowledge Management Algorithms Comparative Studies Cloud Computing Data Management Factor Analysis Privacy and Confidentiality Health Information Medical Records Descriptive Statistics Validity Breast Neoplasms Databases, Health Artificial Intelligence Funding Source ab: Classification is the problem of identifying a set of categories where new data belong, on the basis of a set of training data whose category membership is known. Its application is wide-spread, such as the medical science domain. The issue of the classification knowledge protection has been paid attention increasingly in recent years because of the popularity of cloud environments. In the paper, we propose a Shaking Sorted-Sampling (triple-S) algorithm for protecting the classification knowledge of a dataset. The triple-S algorithm sorts the data of an original dataset according to the projection results of the principal components analysis so that the features of the adjacent data are similar. Then, we generate noise data with incorrect classes and add those data to the original dataset. In addition, we develop an effective positioning strategy, determining the added positions of noise data in the original dataset, to ensure the restoration of the original dataset after removing those noise data. The experimental results show that the disturbance effect of the triple-S algorithm on the CLC, MySVM, and LibSVM classifiers increases when the noise data ratio increases. In addition, compared with existing methods, the disturbance effect of the triple-S algorithm is more significant on MySVM and LibSVM when a certain amount of the noise data added to the original dataset is reached. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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