An Efficient Privacy Preserving 4PC Machine Learning Scheme Based on Secret Sharing

The wide application of machine learning technology makes user data face a serious risk of privacy leakage, and the privacy-preserving distributed machine learning protocol based on secure multi-party computation technology has become a widely concerned research field. In order to obtain a more efficient protocol, Chaudhari et al. proposed the Trident quadrilateral protocol framework. On the basis of the tripartite protocol, an honest participant is introduced as a trusted third party to execute the protocol, and the Swift framework proposed by Koti et al. is to select an honest participant as a trusted third party to complete the protocol through a screening process under the background of a three-party protocol with honest majority of participants. The framework to an honest-majority quadrilateral protocol is generalized. Under such a computing framework, a trusted third party obtains sensitive data of all users, which violates the original intention of secure multi-party computation. To solve this problem, a four-party machine learning protocol based on (2,4) secret sharing is designed. By improving the honest party screening process of the Swift framework, two honest parties can be determined and a semi-honest secure two-party computing protocol which can efficiently complete computing tasks is executed. The protocol transfers 25% of the communication load from the online phase to the offline phase, which improves the efficiency of the online phase of the scheme.