Abstract:

Duo to the enlarging volume and increasingly complex structure of data involved in applications, running the extreme learning machine (ELM) over largescale data becomes a challenging task.In order to reduce the training time while assuring the confidentiality of ELM’s input and output, we present a secure and practical outsourcing mechanism for ELM in cloud computing.In this mechanism, we explicitly divide the ELM into two parts: public part and private part.The latter is executed locally to generate random parameters and do some simple matrix computation while the former part is outsourced by cloud computing that is mainly responsible for calculating the MoorePenrose generalized inverse, the heaviest computational operation. The inverse also serves as the correctness and soundness proof in result verification.We analyze the confidentiality theoretically and the experimental results demonstrate that the proposed mechanism can effectively release customers from heavy computation.

Key words: cloud computing;extreme learning machine;computing outsourcing;data security;privacy-preserving;result verification