Deriving Private Information from Perturbed Data using IQR Based Approach
Conference Proceeding Article
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.
Information Security and Trust
ICDE '06: Proceedings: 22nd International Conference on Data Engineering Workshops, 3-7 April, 2006, Atlanta, Georgia
City or Country
GUO, Songtao; WU, Xintao; and LI, Yingjiu.
Deriving Private Information from Perturbed Data using IQR Based Approach. (2006). ICDE '06: Proceedings: 22nd International Conference on Data Engineering Workshops, 3-7 April, 2006, Atlanta, Georgia. 92. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/321