{"ID":2853320,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.16331","arxiv_id":"2510.16331","title":"Efficient and Privacy-Preserving Binary Dot Product via Multi-Party Computation","abstract":"Striking a balance between protecting data privacy and enabling collaborative computation is a critical challenge for distributed machine learning. While privacy-preserving techniques for federated learning have been extensively developed, methods for scenarios involving bitwise operations, such as tree-based vertical federated learning (VFL), are still underexplored. Traditional mechanisms, including Shamir's secret sharing and multi-party computation (MPC), are not optimized for bitwise operations over binary data, particularly in settings where each participant holds a different part of the binary vector. This paper addresses the limitations of existing methods by proposing a novel binary multi-party computation (BiMPC) framework. The BiMPC mechanism facilitates privacy-preserving bitwise operations, with a particular focus on dot product computations of binary vectors, ensuring the privacy of each individual bit. The core of BiMPC is a novel approach called Dot Product via Modular Addition (DoMA), which uses regular and modular additions for efficient binary dot product calculation. To ensure privacy, BiMPC uses random masking in a higher field for linear computations and a three-party oblivious transfer (triot) protocol for non-linear binary operations. The privacy guarantees of the BiMPC framework are rigorously analyzed, demonstrating its efficiency and scalability in distributed settings.","short_abstract":"Striking a balance between protecting data privacy and enabling collaborative computation is a critical challenge for distributed machine learning. While privacy-preserving techniques for federated learning have been extensively developed, methods for scenarios involving bitwise operations, such as tree-based vertical...","url_abs":"https://arxiv.org/abs/2510.16331","url_pdf":"https://arxiv.org/pdf/2510.16331v1","authors":"[\"Fatemeh Jafarian Dehkordi\",\"Elahe Vedadi\",\"Alireza Feizbakhsh\",\"Yasaman Keshtkarjahromi\",\"Hulya Seferoglu\"]","published":"2025-10-18T03:35:42Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.CC\"]","methods":"[]","has_code":false}