Title

A Privacy Preserving Probabilistic Neural Network For Horizontally Partitioned Databases

Abstract

In this paper, we present a version of the Probabilistic Neural Network (PNN) that is capable of operating on a distributed database that is horizontally partitioned. It does so in a way that is privacy-preserving: that is, a test point can be evaluated by the algorithm without any party knowing the data owned by the other parties. We present an analysis of this algorithm from the standpoints of security and computational performance. Finally, we provide performance results of an implementation of this privacy preserving, distributed PNN algorithm. ©2007 IEEE.

Publication Date

12-1-2007

Publication Title

IEEE International Conference on Neural Networks - Conference Proceedings

Number of Pages

1554-1559

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/IJCNN.2007.4371189

Socpus ID

51749121564 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/51749121564

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