TY - GEN
T1 - ONN the use of neural networks for data privacy
AU - Pont-Tuset, Jordi
AU - Medrano-Gracia, Pau
AU - Nin, Jordi
AU - Larriba-Pey, Josep L.
AU - Muntés-Mulero, Victor
PY - 2008
Y1 - 2008
N2 - The need for data privacy motivates the development of new methods that allow to protect data minimizing the disclosure risk without losing valuable statistical information. In this paper, we propose a new protection method for numerical data called Ordered Neural Networks (ONN). ONN presents a new way to protect data based on the use of Artificial Neural Networks (ANNs). The main contribution of ONN is a new strategy for preprocessing data so that the ANNs are not capable of accurately learning the original data set. Using the results obtained by the ANNs, ONN generates a new data set similar to the original one without disclosing the real sensible values. We compare our method to the best methods presented in the literature, using data provided by the US Census Bureau. Our experiments show that ONN outperforms the previous methods proposed in the literature, proving that the use of ANNs is convenient to protect the data efficiently without losing the statistical properties of the set.
AB - The need for data privacy motivates the development of new methods that allow to protect data minimizing the disclosure risk without losing valuable statistical information. In this paper, we propose a new protection method for numerical data called Ordered Neural Networks (ONN). ONN presents a new way to protect data based on the use of Artificial Neural Networks (ANNs). The main contribution of ONN is a new strategy for preprocessing data so that the ANNs are not capable of accurately learning the original data set. Using the results obtained by the ANNs, ONN generates a new data set similar to the original one without disclosing the real sensible values. We compare our method to the best methods presented in the literature, using data provided by the US Census Bureau. Our experiments show that ONN outperforms the previous methods proposed in the literature, proving that the use of ANNs is convenient to protect the data efficiently without losing the statistical properties of the set.
KW - Artificial neural networks
KW - Data preprocessing
KW - Perturbative protection methods
KW - Privacy in statistical databases
UR - http://www.scopus.com/inward/record.url?scp=38549118438&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-77566-9_55
DO - 10.1007/978-3-540-77566-9_55
M3 - Conference contribution
AN - SCOPUS:38549118438
SN - 354077565X
SN - 9783540775652
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 634
EP - 645
BT - SOFSEM 2008
PB - Springer Verlag
T2 - SOFSEM 2008 - 34th Conference on Current Trends in Theory and Practice of Computer Science
Y2 - 19 January 2008 through 25 January 2008
ER -