An ELM-based privacy preserving protocol for cloud systems
Hashimoto Masato, Yuya Kaneda, Qiangfu Zhao · 2016
In this paper, we propose a privacy preserving protocol for cloud system utilization based on extreme learning machine (ELM). The purpose is to implement aware agents (A-agents) on portable/wearable computing devices (P/WCD). The proposed protocol is useful to reduce the calculation cost on the P/WCD. The basic idea of the protocol is to divide an ELM-based A-agent into two parts, one containing the weights of hidden layer(s) and the other containing the weights of the output layer. The former is implemented in the remote server and the latter is implemented in the P/WCD. In addition, the input data are first encrypted in the P/WCD using transposition cipher, and then sent to the server. Because the server can only “see” random weights and encrypted data, the user intention and privacy can be protected. In addition, since part of the computations is executed on the server, the cost for implementing A-agents in the P/WCD can be reduced. Experimental results on several public databases show that the proposed protocol is useful if the dimension of the input data is high.