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...

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 2008
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/749
http://dx.doi.org/10.1007/s10115-008-0123-9
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-1748
record_format dspace
spelling 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
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
Determining Error Bounds for Spectral Filtering Based Reconstruction Methods in Privacy Preserving Data Mining
description 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.
format text
author GUO, Songtao
Wu, Xintao
LI, Yingjiu
author_facet GUO, Songtao
Wu, Xintao
LI, Yingjiu
author_sort 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
title_full_unstemmed Determining Error Bounds for Spectral Filtering Based Reconstruction Methods in Privacy Preserving Data Mining
title_sort determining error bounds for spectral filtering based reconstruction methods in privacy preserving data mining
publisher Institutional Knowledge at Singapore Management University
publishDate 2008
url https://ink.library.smu.edu.sg/sis_research/749
http://dx.doi.org/10.1007/s10115-008-0123-9
_version_ 1770570699230937088