AliasShare: A Plug-and-Play Module for Efficient Embedding Operations in MPC Workflows

We propose AliasShare, a method designed to accelerate embedding operations in secure multi-party computation (MPC) for private information retrieval (PIR) in ML applications. AliasShare applies a one-to-many mapping for high-frequency tokens along with shuffling in the offline stage, reducing the need to transmit full embeddings and thereby lowering client's online upload communication overhead by $d\times$ ($d$ represents embedding dimension). Meanwhile, it avoids performing costly secret embedding multiplication on the server, mitigating server computation overhead to plaintext-level. Our code is available at https://anonymous.4open.science/r/AliasShare-main-BC51.