STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
A new scheme on privacy-preserving data classification
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Privacy-preserving SVM classification
Knowledge and Information Systems
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Privacy-preserving data mining in the malicious model
International Journal of Information and Computer Security
New Methods in Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
RBF neural network center selection based on Fisher ratio class separability measure
IEEE Transactions on Neural Networks
Neuron selection for RBF neural network classifier based on data structure preserving criterion
IEEE Transactions on Neural Networks
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The iris signature is used in "Iridology", for person identification and calculation of torsional eye movements in video-oculography. Iris data are expensive and difficult to be acquired and their amount plays an important role in recognition accuracy when data mining methods are used. However, privacy issues often restrict open exchange of data between stakeholders. The present article presents a privacy-preserving neural network protocol, for horizontally-partitioned datasets, i.e. datasets that share common attributes but contain different records at each party. The proposed protocol assumes a malicious user model and does not use homomorphic cryptographic methods which are inherently only suited for a semi-trusted user environment. The performance analysis shows that the communication overhead is low enough to warrant its use while the computational complexity is identical in most cases with the centralized computation scenario (e.g. a trusted third party). The accuracy of the output model is only marginally subpar to a centralized computation on the union of all datasets. Another important aim of this work is to search proper choice of the part of the iris signature for person identification and calculating torsional eye movements. Also, estimate changes of the iris contour, sections of the iris and elements of the iris signature. The mathematical model of formation of the iris image on the plane was compared with the real image of the iris.