Computer-aided verification provides effective means of analyzing the security of cryptographic primitives. However, it has remained a challenge to achieve fully automated analyses yielding guarantees that hold against computational (rather than symbolic) attacks. This paper meets this challenge for public-key encryption schemes built from trapdoor permutations and hash functions. Using a novel combination of techniques from computational and symbolic cryptography, we present proof systems for analyzing the chosen-plaintext and chosen-ciphertext security of such schemes in the random oracle model. Building on these proof systems, we develop a toolset that bundles together fully automated proof and attack finding algorithms. We use this toolset to build a comprehensive database of encryption schemes that records attacks against insecure schemes, and proofs with concrete bounds for secure ones.

Existing pattern-based compiler technology is unable to effectively exploit the full potential of SIMD architectures. We present a new program synthesis based technique for auto-vectorizing performance critical innermost loops. Our synthesis technique is applicable to a wide range of loops, consistently produces performant SIMD code, and generates correctness proofs for the output code. The synthesis technique, which leverages existing work on relational verification methods, is a novel combination of deductive loop restructuring, synthesis condition generation and a new inductive synthesis algorithm for producing loop-free code fragments. The inductive synthesis algorithm wraps an optimized depth-first exploration of code sequences inside a CEGIS loop. Our technique is able to quickly produce SIMD implementations (up to 9 instructions in 0.12 seconds) for a wide range of fundamental looping structures. The resulting SIMD implementations outperform the original loops by 2.0x-3.7x.