MDTL: Maliciously Secure Distributed Transfer Learning Based on Replicated Secret Sharing

Abstract

As data continues to grow at an unprecedented rate and informationization accelerates, concerns over data privacy have become more prominent. In image classification tasks, the challenge of insufficient labeled data is common. Transfer learning, an effective and important machine learning method, can address this issue by leveraging knowledge from the source domain to enhance performance in the target domain. However, existing privacy-preserving transfer learning schemes continue to face challenges related to low security and multiple rounds of communication. In the following works, we design a three-party privacy-preserving transfer learning protocol based on the Joint Distributed Adaptation (JDA) algorithm, which ensures malicious security under an honest majority model. To realize this protocol, we designed a series of sub-protocols for constant-round communication, including distributed solving of eigenvalues and eigenvectors based on replicated secret sharing techniques. Compared to existing work, our protocol requires fewer rounds and satisfies malicious security. We provide formal security proofs for the designed protocol and assess its performance using real datasets. Our protocol for computing the eigenvalues of matrices in a given dimension is approximately 2.5 times faster than existing methods. The results of the experiments demonstrate both the security and effectiveness of the proposed approach.