A Privacy Preserving Probabilistic Neural Network for Horizontally Partitioned Databases
Jimmy Secretan, Michael Georgiopoulos, José Villaverde Castro · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · 2007
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.