Paper 2023/089

COMBINE: COMpilation and Backend-INdependent vEctorization for Multi-Party Computation

Benjamin Levy, Rensselaer Polytechnic Institute
Muhammad Ishaq, Purdue University West Lafayette
Ben Sherman, Rensselaer Polytechnic Institute
Lindsey Kennard, STR
Ana Milanova, Rensselaer Polytechnic Institute
Vassilis Zikas, Purdue University West Lafayette

Recent years have witnessed significant advances in programming technology for multi-party computation (MPC), bringing MPC closer to practice and wider applicability. Typical MPC programming frameworks focus on either front-end language design (e.g., Wysteria, Viaduct, SPDZ), or back-end protocol implementation (e.g., ABY, MOTION, SPDZ). We propose a methodology for an MPC compilation toolchain, which by mimicking the compilation methodology of classical compilers enables middle-end (i.e., machine-independent) optimizations, yielding significant improvements. We advance an intermediate language, which we call MPC-IR that can be viewed as the analogue of (enriched) Static Single Assignment (SSA) form. MPC-IR enables backend-independent optimizations in a close analogy to machine-independent optimizations in classical compilers. To demonstrate our approach, we focus on a specific backend-independent optimization, SIMD-vectorization: We devise a novel classical-compiler-inspired automatic SIMD vectorization on MPC-IR. To demonstrate backend independence and quality of our optimization, we evaluate our approach with two mainstream backend frameworks that support multiple types of MPC protocols, namely MOTION and MP-SPDZ, and show significant improvements across the board.

Note: minor typos fixed to improve readability

Available format(s)
Publication info
Published elsewhere. Major revision. ACM Conference on Computer and Communications Security (CCS) 2023
applied cryptographyprogram and binary analysissystems securitymultiparty computationcompilers
Contact author(s)
levb3 @ rpi edu
ishaqm @ purdue edu
shermb @ rpi edu
fireelemental ne @ gmail com
milanova @ cs rpi edu
vzikas @ purdue edu
2023-12-20: last of 8 revisions
2023-01-24: received
See all versions
Short URL
Creative Commons Attribution


      author = {Benjamin Levy and Muhammad Ishaq and Ben Sherman and Lindsey Kennard and Ana Milanova and Vassilis Zikas},
      title = {COMBINE: COMpilation and Backend-INdependent vEctorization for Multi-Party Computation},
      howpublished = {Cryptology ePrint Archive, Paper 2023/089},
      year = {2023},
      doi = {10.1145/3576915.3623181},
      note = {\url{}},
      url = {}
Note: In order to protect the privacy of readers, does not use cookies or embedded third party content.