A Formal Model for Secure Multiparty Computation

Although Secure Multiparty Computation (SMC) has seen considerable development in recent years, its use is challenging, resulting in complex code which obscures whether the security properties or correctness guarantees hold in practice. For this reason, several works have investigated the use of formal methods to provide guarantees for SMC systems. However, these approaches have been applied mostly to domain specific languages (DSL), neglecting general-purpose approaches. In this paper, we consider a formal model for an SMC system for annotated C programs. We choose C due to its popularity in the cryptographic community and being the only general-purpose language for which SMC compilers exist. Our formalization supports all key features of C -- including private-conditioned branching statements, mutable arrays (including out of bound array access), pointers to private data, etc. We use this formalization to characterize correctness and security properties of annotated C, with the latter being a form of non-interference on execution traces. We realize our formalism as an implementation in the PICCO SMC compiler and provide evaluation results on SMC programs written in C

Optimizing Secure Computation Programs with Private Conditionals

Secure multiparty computation platforms are often provided with a programming language that allows to write privacy-preserving applications without thinking of the underlying cryptography. The control flow of these programs is expensive to hide, hence they typically disallow branching on private values. The application programmers have to specify their programs in terms of allowed constructions, either using ad-hoc methods to avoid such branchings, or the general methodology of executing all branches and obliviously selecting the effects of one at the end. There may be compiler support for the latter. The execution of all branches introduces significant computational overhead. If the branches perform similar private operations, then it may make sense to compute repeating patterns only once, even though the necessary bookkeeping also has overheads. In this paper, we propose a program optimization doing exactly that, allowing the overhead of private conditionals to be reduced. The optimization is quite general, and can be applied to various privacy-preserving platforms