  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.
