Secure Computation Framework for Multiple Data Providers Against Malicious Adversaries

TL;DR

This paper introduces a secure computation framework for multiple data providers that ensures privacy and correctness against malicious adversaries, using cut-and-choose and garbled circuits techniques, with practical implementation and evaluation.

Contribution

It presents a novel general framework for secure multi-party computation among multiple data providers in the malicious security model, combining cut-and-choose and garbled circuits techniques.

Findings

Framework is secure against malicious parties and data providers.

Implementation on secure cloud resource auctions demonstrates practicality.

Experimental results show acceptable performance in real-world scenarios.

Abstract

Due to the great development of secure multi-party computation, many practical secure computation schemes have been proposed. As an example, different secure auction mechanisms have been widely studied, which can protect bid privacy while satisfying various economic properties. However, as far as we know, none of them solve the secure computation problems for multiple data providers (e.g., secure cloud resource auctions) in the malicious security model. In this paper, we use the techniques of cut-and-choose and garbled circuits to propose a general secure computation framework for multiple data providers against malicious adversaries. Specifically, our framework checks input consistency with the cut-and-choose paradigm, conducts maliciously secure computations by running two independent garbled circuits, and verifies the correctness of output by comparing two versions of outputs. Theoretical analysis shows that our framework is secure against a malicious computation party, or a subset of malicious data providers. Taking secure cloud resource auctions as an example, we implement our framework. Extensive experimental evaluations show that the performance of the proposed framework is acceptable in practice.