Deriving Private Information from Perturbed Data using IQR Based Approach
Several randomized techniques have been proposed for privacy preserving data mining of continuous data. These approaches generally attempt to hide the sensitive data by randomly modifying the data values using some additive noise and aim to reconstruct the original distribution closely at an aggrega...
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sg-smu-ink.sis_research-13202010-09-24T05:42:03Z Deriving Private Information from Perturbed Data using IQR Based Approach GUO, Songtao WU, Xintao LI, Yingjiu Several randomized techniques have been proposed for privacy preserving data mining of continuous data. These approaches generally attempt to hide the sensitive data by randomly modifying the data values using some additive noise and aim to reconstruct the original distribution closely at an aggregate level. However, one challenge here is whether the reconstructed distribution can be exploited by attackers or snoopers to derive sensitive individual data. This paper presents one simple attack using Inter-Quantile Range on reconstructed distribution. The experimental results show that current random perturbation-based privacy preserving data mining techniques may need a careful scrutiny in order to prevent privacy breaches through this model based inference. 2006-04-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/321 info:doi/10.1109/ICDEW.2006.47 http://dx.doi.org/10.1109/ICDEW.2006.47 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 Deriving Private Information from Perturbed Data using IQR Based Approach |
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Several randomized techniques have been proposed for privacy preserving data mining of continuous data. These approaches generally attempt to hide the sensitive data by randomly modifying the data values using some additive noise and aim to reconstruct the original distribution closely at an aggregate level. However, one challenge here is whether the reconstructed distribution can be exploited by attackers or snoopers to derive sensitive individual data. This paper presents one simple attack using Inter-Quantile Range on reconstructed distribution. The experimental results show that current random perturbation-based privacy preserving data mining techniques may need a careful scrutiny in order to prevent privacy breaches through this model based inference. |
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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 |
Deriving Private Information from Perturbed Data using IQR Based Approach |
title_short |
Deriving Private Information from Perturbed Data using IQR Based Approach |
title_full |
Deriving Private Information from Perturbed Data using IQR Based Approach |
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Deriving Private Information from Perturbed Data using IQR Based Approach |
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Deriving Private Information from Perturbed Data using IQR Based Approach |
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deriving private information from perturbed data using iqr based approach |
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Institutional Knowledge at Singapore Management University |
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2006 |
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https://ink.library.smu.edu.sg/sis_research/321 http://dx.doi.org/10.1109/ICDEW.2006.47 |
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