Design method of secure computing protocol for deep neural network

Aiming at the information leakage problem in the process of deep neural network model calculation, a series of secure and efficient interactive computing protocols were designed between two non-collusive edge servers in combination with the additive secret sharing scheme. Since the nonlinear function cannot be split directly, a set of basic conversion protocols were proposed to realize the secure conversion of additive and multiplicative shares. After a few invokes, the power, comparison, exponential, logarithm, division and other low-level functions can be calculated securely. Due to the characteristics of data transfer and computation, the proposed protocols can be extended to array computation. Theoretical analysis ensures the correctness, efficiency and security of these protocols. The experimental results show that the error of these protocols is negligible, and the computational costs and communication overhead are better than the existing schemes.

Abstract licence: CC BY-NC-ND

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