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More Hybrid and Secure Protection of Statistical Data Sets
Sept.-Oct. 2012 (vol. 9 no. 5)
pp. 727-740
ASCII Text
x 
 
Javier Herranz, Jordi Nin, Marc Solé, "More Hybrid and Secure Protection of Statistical Data Sets," IEEE Transactions on Dependable and Secure Computing, vol. 9, no. 5, pp. 727-740, Sept.-Oct., 2012.
 
 
BibTex
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@article{ 10.1109/TDSC.2012.40,
author = {Javier Herranz and Jordi Nin and Marc Solé},
title = {More Hybrid and Secure Protection of Statistical Data Sets},
journal ={IEEE Transactions on Dependable and Secure Computing},
volume = {9},
number = {5},
issn = {1545-5971},
year = {2012},
pages = {727-740},
doi = {http://doi.ieeecomputersociety.org/10.1109/TDSC.2012.40},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Dependable and Secure Computing
TI - More Hybrid and Secure Protection of Statistical Data Sets
IS - 5
SN - 1545-5971
SP727
EP740
EPD - 727-740
A1 - Javier Herranz,
A1 - Jordi Nin,
A1 - Marc Solé,
PY - 2012
KW - Privacy
KW - Security
KW - Data privacy
KW - Couplings
KW - Computational modeling
KW - Databases
KW - Generators
KW - interval disclosure risk.
KW - Statistical data sets protection
KW - synthetic methods
KW - hybrid methods
VL - 9
JA - IEEE Transactions on Dependable and Secure Computing
ER -
 
Javier Herranz, Universitat Politècnica de Catalunya, Barcelona
Jordi Nin, Universitat Politècnica de Catalunya, Barcelona
Marc Solé, Universitat Politècnica de Catalunya, Barcelona
Different methods and paradigms to protect data sets containing sensitive statistical information have been proposed and studied. The idea is to publish a perturbed version of the data set that does not leak confidential information, but that still allows users to obtain meaningful statistical values about the original data. The two main paradigms for data set protection are the classical one and the synthetic one. Recently, the possibility of combining the two paradigms, leading to a hybrid paradigm, has been considered. In this work, we first analyze the security of some synthetic and (partially) hybrid methods that have been proposed in the last years, and we conclude that they suffer from a high interval disclosure risk. We then propose the first fully hybrid SDC methods; unfortunately, they also suffer from a quite high interval disclosure risk. To mitigate this, we propose a postprocessing technique that can be applied to any data set protected with a synthetic method, with the goal of reducing its interval disclosure risk. We describe through the paper a set of experiments performed on reference data sets that support our claims.

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Index Terms:
Privacy,Security,Data privacy,Couplings,Computational modeling,Databases,Generators,interval disclosure risk.,Statistical data sets protection,synthetic methods,hybrid methods
Citation:
Javier Herranz, Jordi Nin, Marc Solé, "More Hybrid and Secure Protection of Statistical Data Sets," IEEE Transactions on Dependable and Secure Computing, vol. 9, no. 5, pp. 727-740, Sept.-Oct. 2012, doi:10.1109/TDSC.2012.40
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