Paper 2026/977

ThriftyMPC: Reducing the Cost of Large-Scale MPC in the Cloud

Sahbaaz Ansari, University of Luxembourg

Tushar M. Jois, City College of New York

Rosario Gennaro, City College of New York

Gamze Gursoy, Columbia University, University of Cambridge

Gabriel Kaptchuk, University of Maryland, College Park

Moti Yung, Columbia University, Google (United States)

Diogo Barradas, University of Waterloo

Abstract

Cloud computing has become the standard for large-scale computation, offering elastic scalability and on-demand resources that exceed typical on-premise capabilities. However, many large-scale computations over sensitive data -- such as genome-wide association studies (GWAS) -- face significant barriers to cloud adoption due to privacy concerns and regulatory constraints. While cryptographic primitives like multi-party computation can alleviate these concerns through provable privacy guaranties, their substantial communication and computational overhead can make cloud deployment cost-prohibitive. To address both privacy and cost constraints, we present ThriftyMPC. ThriftyMPC is a framework that leverages spot instances (ephemeral cloud compute at reduced rates) to enable cost-effective, privacy-preserving computation at scale by combining secure multi-party computation with preemption-tolerant execution. We introduce a formal model for multi-party execution under ephemeral compute conditions, demonstrate how ThriftyMPC handles spot instance preemptions while maintaining cryptographic security guaranties, and provide a formal discussion of these guaranties. Our evaluations on realistic GWAS-inspired workloads over the Google Cloud Platform demonstrate robust execution despite spot instance churn, and show significant cost reduction compared to the state-of-the-art multi-party computation framework (MP-SPDZ) run traditionally using on-demand instances. We show that leveraging multi-party computation on spot instances makes privacy-preserving computation economically viable, enabling organizations to harness the cloud for sensitive workloads previously confined to isolated, on-premise deployments.

@misc{cryptoeprint:2026/977,
      author = {David Inyangson and Sahbaaz Ansari and Tushar M. Jois and Rosario Gennaro and Gamze Gursoy and Gabriel Kaptchuk and Moti Yung and Diogo Barradas},
      title = {{ThriftyMPC}: Reducing the Cost of Large-Scale {MPC} in the Cloud},
      howpublished = {Cryptology {ePrint} Archive, Paper 2026/977},
      year = {2026},
      url = {https://eprint.iacr.org/2026/977}
}