PPEPV‐RDL: Achieving Privacy‐Preserving, Efficient, Publicly Verifiable Rational Delegation Learning

Delegation learning is an implementation approach within privacy‐preserving machine learning (PPML). It refers to that the delegator outsources the training task to servers with strong computing power due to its own limited computing resources, and the servers return the correct training results and get the service revenue. However, it is difficult to balance the security and usability of delegation learning, which brings great technical challenges to the design of security mechanisms for delegation learning. In this paper, we propose a privacy‐preserving, efficient and publicly verifiable scheme for rational delegation learning (PPEPV‐RDL). First, we demonstrate that in the absence of an effective incentive mechanism, rational participants in delegation learning inevitably fall into a prisoner's dilemma, which leads to a collapse of protocol execution where no participant performs correctly. Second, we design an incentive mechanism and construct a game‐theoretic model for PPEPV‐RDL. We prove the existence of a unique sequential equilibrium, which ensures that rational participants have no incentive to deviate from honest behaviour, thereby achieving incentive compatibility. Finally, we combine the universal composability (UC) theory to construct the ideal function of rational delegation learning and prove the security of PPEPV‐RDL. Simulation experiments show that PPEPV‐RDL not only achieves public verifiability but also maintains data privacy while improving computational efficiency and reducing computational overhead.