Symbolic security of garbled circuits

We present the first computationally sound symbolic analysis of Yao\u27s garbled circuit construction for secure two party computation. Our results include an extension of the symbolic language for cryptographic expressions from previous work on computationally sound symbolic analysis, and a soundness theorem for this extended language. We then demonstrate how the extended language can be used to formally specify not only the garbled circuit construction, but also the formal (symbolic) simulator required by the definition of security. The correctness of the simulation is proved in a purely syntactical way, within the symbolic model of cryptography, and then translated into a concrete computational indistinguishability statement via our general computational soundness theorem. We also implement our symbolic security framework and the garbling scheme in Haskell, and our experiment shows that the symbolic analysis performs well and can be done within several seconds even for large circuits that are useful for real world applications

Symbolic Encryption with Pseudorandom Keys

We give an efficient decision procedure that, on input two (acyclic) cryptographic expressions making arbitrary use of an encryption scheme and a (length doubling) pseudorandom generator, determines (in polynomial time) if the two expressions produce computationally indistinguishable distributions for any pseudorandom generator and encryption scheme satisfying the standard security notions of pseudorandomness and indistinguishability under chosen plaintext attack. The procedure works by mapping each expression to a symbolic pattern that captures, in a fully abstract way, the information revealed by the expression to a computationally bounded observer. We then prove that if any two (possibly cyclic) expressions are mapped to the same pattern, then the associated distributions are indistinguishable. At the same time, if the expressions are mapped to different symbolic patterns and do not contain encryption cycles, there are secure pseudorandom generators and encryption schemes for which the two distributions can be distinguished with overwhelming advantage

A High-Assurance Evaluator for Machine-Checked Secure Multiparty Computation

Secure Multiparty Computation (MPC) enables a group of $n$ distrusting parties to jointly compute a function using private inputs. MPC guarantees correctness of computation and confidentiality of inputs if no more than a threshold $t$ of the parties are corrupted. Proactive MPC (PMPC) addresses the stronger threat model of a mobile adversary that controls a changing set of parties (but only up to $t$ at any instant), and may eventually corrupt all $n$ parties over a long time. This paper takes a first stab at developing high-assurance implementations of (P)MPC. We formalize in EasyCrypt, a tool-assisted framework for building high-confidence cryptographic proofs, several abstract and reusable variations of secret sharing and of (P)MPC protocols building on them. Using those, we prove a series of abstract theorems for the proactive setting. We implement and perform computer-checked security proofs of concrete instantiations of the required (abstract) protocols in EasyCrypt. We also develop a new tool-chain to extract high-assurance executable implementations of protocols formalized and verified in EasyCrypt. Our tool-chain uses Why as an intermediate tool, and enables us to extract executable code from our (P)MPC formalizations. We conduct an evaluation of the extracted executables by comparing their performance to performance of manually implemented versions using Python-based Charm framework for prototyping cryptographic schemes. We argue that the small overhead of our high-assurance executables is a reasonable price to pay for the increased confidence about their correctness and security