Work

Efficient Coded Multi-Party Computation at Edge Networks

Year: 2023

Type: article

Abstract: Multi-party computation (MPC) is promising for designing privacy-preserving machine learning algorithms at edge networks. An emerging approach is coded-MPC (CMPC), which advocates the use of coded computation to improve the performance of MPC in terms of the required number of workers involved in computations. The current approach for designing CMPC algorithms is to merely combine efficient coded computation constructions with MPC. We show that this approach fails short of being efficient; e.g., entangled polynomial codes are not necessarily better than PolyDot codes in MPC setting, while they are always better for coded computation. Motivated by this observation, we propose a new construction; Adaptive Gap Entangled (AGE) polynomial codes for MPC. We show through analysis and simulations that MPC with AGE codes always perform better than existing CMPC algorithms in terms of the required number of workers as well as computation, storage, and communication overhead.

Source: IEEE Transactions on Information Forensics and Security

DOI: 10.1109/tifs.2023.3326970

Authors: Elahe Vedadi, Yasaman Keshtkarjahromi, Hulya Seferoglu

Institution: University of Illinois Chicago

Language: English

Topic: Cryptography and Data Security

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,345

Funders: National Science Foundation, Seagate Technology