On the Lower Bound of Reconstruction Error for Spectral Filtering Based Privacy Preserving Data Mining
Additive Randomization has been a primary tool to hide sensitive private information during privacy preserving data mining. The previous work based on Spectral Filtering empirically showed that individual data can be separated from the perturbed one and as a result privacy can be seriously compromis...
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Main Authors: | GUO, Songtao, Wu, Xintao, LI, Yingjiu |
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格式: | text |
語言: | English |
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Institutional Knowledge at Singapore Management University
2006
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在線閱讀: | https://ink.library.smu.edu.sg/sis_research/322 http://dx.doi.org/10.1007/11871637_51 |
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機構: | Singapore Management University |
語言: | English |
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