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...

Full description

Saved in:
Bibliographic Details
Main Authors: GUO, Songtao, WU, Xintao, LI, Yingjiu
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2006
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/321
http://dx.doi.org/10.1109/ICDEW.2006.47
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-1320
record_format dspace
spelling 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
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Information Security
spellingShingle Information Security
GUO, Songtao
WU, Xintao
LI, Yingjiu
Deriving Private Information from Perturbed Data using IQR Based Approach
description 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.
format text
author GUO, Songtao
WU, Xintao
LI, Yingjiu
author_facet GUO, Songtao
WU, Xintao
LI, Yingjiu
author_sort 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
title_fullStr Deriving Private Information from Perturbed Data using IQR Based Approach
title_full_unstemmed Deriving Private Information from Perturbed Data using IQR Based Approach
title_sort deriving private information from perturbed data using iqr based approach
publisher Institutional Knowledge at Singapore Management University
publishDate 2006
url https://ink.library.smu.edu.sg/sis_research/321
http://dx.doi.org/10.1109/ICDEW.2006.47
_version_ 1770570385748656128