Paper Info
Title
Reconstruction attack through classifier analysis
Abstract
In this paper, we introduce a novel inference attack that we coin as the reconstruction attack whose objective is to reconstruct a probabilistic version of the original dataset on which a classifier was learnt from the description of this classifier and possibly some auxiliary information. In a nutshell, the reconstruction attack exploits the structure of the classifier in order to derive a probabilistic version of dataset on which this model has been trained. Moreover, we propose a general framework that can be used to assess the success of a reconstruction attack in terms of a novel distance between the reconstructed and original datasets. In case of multiple releases of classifiers, we also give a strategy that can be used to merge the different reconstructed datasets into a single coherent one that is closer to the original dataset than any of the simple reconstructed datasets. Finally, we give an instantiation of this reconstruction attack on a decision tree classifier that was learnt using the algorithm C4.5 and evaluate experimentally its efficiency. The results of this experimentation demonstrate that the proposed attack is able to reconstruct a significant part of the original dataset, thus highlighting the need to develop new learning algorithms whose output is specifically tailored to mitigate the success of this type of attack.
Year
DOI
Venue
2012
10.1007/978-3-642-31540-4_21
DBSec
Keywords
DocType
Volume
classifier analysis,different reconstructed datasets,original datasets,original dataset,simple reconstructed datasets,proposed attack,novel distance,reconstruction attack,probabilistic version,decision tree classifier,novel inference attack,privacy,data mining,decision trees
Conference
7371
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
6
3
Name
Order
Citations
PageRank
Sébastien Gambs138131.33
Ahmed Gmati200.34
Michel Hurfin326629.30