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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wei Lu |
| Copyright Year | 2009 |
| Abstract | In this paper, a privacy preserving classification algorithm based random Diffusion Map is presented. We first alter the selection of the parameter dimension $d$ and metaparameter fixed value $\varepsilon$ for satisfying the security of privacy-preserving classification. Further the sensitive attributes are embedded into random(even higher) dimension feature space using random Diffusion Map, thus the sensitive attributes are transformed and protected. Because the transformed space dimension $d$ and the $\varepsilon$ are both stochastic, this algorithm is not easily be breached. In addition, diffusion Map can keep topology structure of dataset, so the classification precision after encryption are kept well. The experiment shows that the present method can provide sensitive information enough protect without much loss of the classification precision. |
| Starting Page | 318 |
| Ending Page | 321 |
| File Size | 320014 |
| Page Count | 4 |
| File Format | |
| ISBN | 9780769538105 |
| DOI | 10.1109/SKG.2009.38 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-10-12 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Data privacy Stochastic processes privacy preserving Classification algorithms Topology classification Information technology Information security topology structure Euclidean distance random Diffusion Map Cryptography Protection Joining processes |
| Content Type | Text |
| Resource Type | Article |
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