Determining Error Bounds for Spectral Filtering Based Reconstruction Methods in Privacy Preserving Data Mining
Additive randomization has been a primary tool for hiding sensitive private information. Previous work empirically showed that individual data values can be approximately reconstructed from the perturbed values, using spectral filtering techniques. This poses a serious threat of privacy breaches. In...
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sg-smu-ink.sis_research-17482010-11-26T07:24:03Z Determining Error Bounds for Spectral Filtering Based Reconstruction Methods in Privacy Preserving Data Mining GUO, Songtao Wu, Xintao LI, Yingjiu Additive randomization has been a primary tool for hiding sensitive private information. Previous work empirically showed that individual data values can be approximately reconstructed from the perturbed values, using spectral filtering techniques. This poses a serious threat of privacy breaches. In this paper we conduct a theoretical study on how the reconstruction error varies, for different types of additive noise. In particular, we first derive an upper bound for the reconstruction error using matrix perturbation theory. Attackers who use spectral filtering techniques to estimate the true data values may leverage this bound to determine how close their estimates are to the original data. We then derive a lower bound for the reconstruction error, which can help data owners decide how much noise should be added to satisfy a given threshold of the tolerated privacy breach. 2008-11-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/749 info:doi/10.1007/s10115-008-0123-9 http://dx.doi.org/10.1007/s10115-008-0123-9 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Information Security |
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Information Security GUO, Songtao Wu, Xintao LI, Yingjiu Determining Error Bounds for Spectral Filtering Based Reconstruction Methods in Privacy Preserving Data Mining |
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Additive randomization has been a primary tool for hiding sensitive private information. Previous work empirically showed that individual data values can be approximately reconstructed from the perturbed values, using spectral filtering techniques. This poses a serious threat of privacy breaches. In this paper we conduct a theoretical study on how the reconstruction error varies, for different types of additive noise. In particular, we first derive an upper bound for the reconstruction error using matrix perturbation theory. Attackers who use spectral filtering techniques to estimate the true data values may leverage this bound to determine how close their estimates are to the original data. We then derive a lower bound for the reconstruction error, which can help data owners decide how much noise should be added to satisfy a given threshold of the tolerated privacy breach. |
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GUO, Songtao Wu, Xintao LI, Yingjiu |
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GUO, Songtao Wu, Xintao LI, Yingjiu |
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GUO, Songtao |
title |
Determining Error Bounds for Spectral Filtering Based Reconstruction Methods in Privacy Preserving Data Mining |
title_short |
Determining Error Bounds for Spectral Filtering Based Reconstruction Methods in Privacy Preserving Data Mining |
title_full |
Determining Error Bounds for Spectral Filtering Based Reconstruction Methods in Privacy Preserving Data Mining |
title_fullStr |
Determining Error Bounds for Spectral Filtering Based Reconstruction Methods in Privacy Preserving Data Mining |
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Determining Error Bounds for Spectral Filtering Based Reconstruction Methods in Privacy Preserving Data Mining |
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determining error bounds for spectral filtering based reconstruction methods in privacy preserving data mining |
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Institutional Knowledge at Singapore Management University |
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2008 |
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https://ink.library.smu.edu.sg/sis_research/749 http://dx.doi.org/10.1007/s10115-008-0123-9 |
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