Privacy Preserving Back-Propagation Neural Network Learning Made Practical with Cloud Computing

Abstract

Abstract-To improve the accuracy of learning result, in practice multiple parties may collaborate through conducting joint Backpropagation neural network learning on the union of their respective data sets. During this process no party wants to disclose her/his private data to others. Existing schemes supporting this kind of collaborative learning are either limited in the way of data partition or just consider two parties. There lacks a solution that allows two or more parties, each with an arbitrarily partitioned data set, to collaboratively conduct the learning. This paper solves this open problem by utilizing the power of cloud computing. In our proposed scheme, each party encrypts his/her private data locally and uploads the ciphertexts into the cloud. The cloud then executes most of the operations pertaining to the learning algorithms over ciphertexts without knowing the original private data. By securely offloading the expensive operations to the cloud, we keep the computation and communication costs on each party minimal and independent to the number of participants. To support flexible operations over ciphertexts, we adopt and tailor the BGN'doubly homomorphic' encryption algorithm for the multi-party setting. Numerical analysis and experiments on commodity cloud show that our scheme is secure, efficient and accurate.

Review and analysis of hidden neuron number effect of shallow backpropagation neural networks

“…Lilik [23] and Dimililer [24] used it to detect the lung cancer on CT scan images. Comparison of workload prediction model for cloud computing by Kumar et al [6], privacypreserving using modified BPNN for cloud computing by Yuan et al [25], and a system in order to detect the sub-pixel land change for remotely sensed images by Wu et al [26] proposed using BPNN. Also, nonlinearity compensation of a photonic transducer-based optical current sensor in [27] and classification of four varieties of bulk rice grain images in [3] used BPNN.…”

Parallelizing Backpropagation Neural Network Using MapReduce and Cascading Model

“…Also several researchers tried to solve the scale issue with combing cloud computing techniques. For example, Yuan and Yu proposed a privacy preserving BPNN in the cloud computing environment [10]. The authors aimed at enabling multiple parties to jointly conduct the BPNN learning without revealing their private data.…”

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

“…Recently, researchers have started utilizing parallel and distributed computing technologies such as cloud computing to solve the computation bottleneck of a large neural network [17–19]. Yuan and Yu [20] employed cloud computing mainly for exchange of privacy data in a BPNN implementation in processing ciphered text classification tasks. However, cloud computing as a computing paradigm simply offers infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS).…”