Reconstruction attack through classifier analysis

Sébastien Gambs 1, 2 Ahmed Gmati 1 Michel Hurfin 1
1 CIDRE - Confidentialité, Intégrité, Disponibilité et Répartition
CentraleSupélec, Inria Rennes – Bretagne Atlantique , IRISA-D1 - SYSTÈMES LARGE ÉCHELLE
2 CIDER
IRISA-D1 - SYSTÈMES LARGE ÉCHELLE
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.
Document type :
Conference papers
Nora Cuppens-Boulahia; Frédéric Cuppens; Joaquin Garcia-Alfaro. 26th Conference on Data and Applications Security and Privacy (DBSec), Jul 2012, Paris, France. Springer, Lecture Notes in Computer Science, LNCS-7371, pp.274-281, 2012, Data and Applications Security and Privacy XXVII. 〈10.1007/978-3-642-31540-4_21〉
Liste complète des métadonnées

Cited literature [14 references]  Display  Hide  Download

https://hal.inria.fr/hal-00736945
Contributor : Sébastien Gambs <>
Submitted on : Thursday, June 8, 2017 - 11:24:19 AM
Last modification on : Wednesday, May 16, 2018 - 11:23:35 AM
Document(s) archivé(s) le : Saturday, September 9, 2017 - 12:53:18 PM

File

978-3-642-31540-4_21_Chapter.p...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Sébastien Gambs, Ahmed Gmati, Michel Hurfin. Reconstruction attack through classifier analysis. Nora Cuppens-Boulahia; Frédéric Cuppens; Joaquin Garcia-Alfaro. 26th Conference on Data and Applications Security and Privacy (DBSec), Jul 2012, Paris, France. Springer, Lecture Notes in Computer Science, LNCS-7371, pp.274-281, 2012, Data and Applications Security and Privacy XXVII. 〈10.1007/978-3-642-31540-4_21〉. 〈hal-00736945〉

Share

Metrics

Record views

1810

Files downloads

55