Ethereum is a framework for cryptocurrencies which uses blockchain technology to provide an open global computing platform, called the Ethereum Virtual Machine (EVM). EVM executes bytecode on a simple stack machine. Programmers do not usually write EVM code; instead, they can program in a JavaScript-like language, called Solidity, that compiles to bytecode. Since the main purpose of EVM is to execute smart contracts that manage and transfer digital assets (called Ether), security is of paramount importance. However, writing secure smart contracts can be extremely difficult: due to the openness of Ethereum, both programs and pseudonymous users can call into the public methods of other programs, leading to potentially dangerous compositions of trusted and untrusted code. This risk was recently illustrated by an attack on TheDAO contract that exploited subtle details of the EVM semantics to transfer roughly $50M worth of Ether into the control of an attacker. In this paper, we outline a framework to analyze and verify both the runtime safety and the functional correctness of Ethereum contracts by translation to F , a functional programming language aimed at program verification.

We present a new, completely redesigned, version of F ⋆ , a language that works both as a proof assistant as well as a general-purpose, verification-oriented, effectful programming language. In support of these complementary roles, F ⋆ is a dependently typed, higher-order, call-by-value language with primitive effects including state, exceptions, divergence and IO. Although primitive, programmers choose the granularity at which to specify effects by equipping each effect with a monadic, predicate transformer semantics. F ⋆ uses this to efficiently compute weakest preconditions and discharges the resulting proof obligations using a combination of SMT solving and manual proofs. Isolated from the effects, the core of F ⋆ is a language of pure functions used to write specifications and proof terms-its consistency is maintained by a semantic termination check based on a well-founded order. We evaluate our design on more than 55,000 lines of F ⋆ we have authored in the last year, focusing on three main case studies. Showcasing its use as a general-purpose programming language, F ⋆ is programmed (but not verified) in F ⋆ , and bootstraps in both OCaml and F#. Our experience confirms F ⋆ 's pay-as-you-go cost model: writing idiomatic ML-like code with no finer specifications imposes no user burden. As a verification-oriented language, our most significant evaluation of F ⋆ is in verifying several key modules in an implementation of the TLS-1.2 protocol standard. For the modules we considered, we are able to prove more properties, with fewer annotations using F ⋆ than in a prior verified implementation of TLS-1.2. Finally, as a proof assistant, we discuss our use of F ⋆ in mechanizing the metatheory of a range of lambda calculi, starting from the simply typed lambda calculus to System F ω and even µF ⋆ , a sizeable fragment of F ⋆ itself-these proofs make essential use of F ⋆ 's flexible combination of SMT automation and constructive proofs, enabling a tactic-free style of programming and proving at a relatively large scale. Categories and Subject Descriptors D.3.1 [Programming Languages]: Formal Definitions and Theory-Semantics; F.3.1 [Logics and Meanings of Programs]: Specifying and Verifying and Reasoning about Programs-Mechanical verification Keywords verification; proof assistants; effectful programming 1 Henceforth, we refer to the new language presented in this paper as "F ⋆ " while referring to the old, defunct version as "old-F ⋆ ".

We present CRYPTFLOW, a first of its kind system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build three components. Our first component, Athos, is an end-to-end compiler from TensorFlow to a variety of semihonest MPC protocols. The second component, Porthos, is an improved semi-honest 3-party protocol that provides significant speedups for TensorFlow like applications. Finally, to provide malicious secure MPC protocols, our third component, Aramis, is a novel technique that uses hardware with integrity guarantees to convert any semi-honest MPC protocol into an MPC protocol that provides malicious security. The malicious security of the protocols output by Aramis relies on integrity of the hardware and semi-honest security of MPC. Moreover, our system matches the inference accuracy of plaintext TensorFlow.We experimentally demonstrate the power of our system by showing the secure inference of real-world neural networks such as RESNET50 and DENSENET121 over the ImageNet dataset with running times of about 30 seconds for semi-honest security and under two minutes for malicious security. Prior work in the area of secure inference has been limited to semi-honest security of small networks over tiny datasets such as MNIST or CIFAR.

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We present EZPC, a secure two-party computation (2PC) framework that generates efficient 2PC protocols from high-level, easy-to-write programs. EZPC provides formal correctness and security guarantees while maintaining performance and scalability. Previous language frameworks, such as CBMC-GC, ObliVM, SMCL, and Wysteria, generate protocols that use either arithmetic or boolean circuits exclusively. Our compiler is the first to generate protocols that combine both arithmetic and boolean circuits for better performance. We empirically demonstrate that the performance of the protocols generated by EZPC is comparable to or better than (in some cases upto 19x) their state-of-the-art, hand-crafted implementations, while EZPC protocols also outperform their boolean circuits only counterparts by as much as 25x.2 Using swap and disk space is feasible but it causes huge slowdown (Figure 15).