Project Everest began at Microsoft Research in 2016, aiming to spur research in program verification to produce industrial-grade software. In collaboration with INRIA and Carnegie Mellon University, Project Everest’s goal was to produce drop-in verified replacements of secure communications software used in the HTTPS ecosystem, including TLS, the underlying cryptography, and related subprotocols. Now, almost a decade later, we reflect on the project, sharing both its successes and failures, and look ahead to the next decade of program verification research.

The Rust programming language, with its safety guarantees, has established itself as a viable choice for low-level systems programming language over the traditional, unsafe alternatives like C/C++. These guarantees come from a strong ownership-based type system, as well as primitive support for features like closures, pattern matching, etc., that make the code more concise and amenable to reasoning. These unique Rust features also pose a steep learning curve for programmers. This paper presents a tool called RustAssistant that leverages the emergent capabilities of Large Language Models (LLMs) to automatically suggest fixes for Rust compilation errors. RustAssistant uses a careful combination of prompting techniques as well as iteration between an LLM and the Rust compiler to deliver high accuracy of fixes. RustAssistant is able to achieve an impressive peak accuracy of roughly 74% on real-world compilation errors in popular open-source Rust repositories. We also contribute a dataset of Rust compilation errors to enable further research.

Memory safety violations in low-level code, written in languages like C, continues to remain one of the major sources of software vulnerabilities. One method of removing such violations by construction is to port C code to a safe C dialect. Such dialects rely on programmer-supplied annotations to guarantee safety with minimal runtime overhead. This porting, however, is a manual process that imposes significant burden on the programmer and, hence, there has been limited adoption of this technique. The task of porting not only requires inferring annotations, but may also need refactoring/rewriting of the code to make it amenable to such annotations. In this paper, we use Large Language Models (LLMs) towards addressing both these concerns. We show how to harness LLM capabilities to do complex code reasoning as well as rewriting of large codebases. We also present a novel framework for whole-program transformations that leverages lightweight static analysis to break the transformation into smaller steps that can be carried out effectively by an LLM. We implement our ideas in a tool called MSA that targets the CheckedC dialect. We evaluate MSA on several micro-benchmarks, as well as real-world code ranging up to 20K lines of code. We showcase superior performance compared to a vanilla LLM baseline, as well as demonstrate improvement over a state-of-the-art symbolic (non-LLM) technique.

PulseCore is a new program logic suitable for intrinsic proofs of higher-order, stateful, concurrent, dependently typed programs. It provides many of the features of a modern, concurrent separation logic, including dynamically allocated impredicative invariants, higher-order ghost state, step-indexing with later credits, and support for user-defined ghost state constructions. PulseCore is developed foundationally within the F ⋆ programming language with fully mechanized proofs, and is applicable to F ⋆ programs itself. To evaluate our work, we use Pulse , a surface language within F ⋆ for PulseCore , to develop a range of program proofs. Illustrating its suitability for proving higher-order concurrent programs, we present a verified library for task pools in the style of OCaml5, together with some verified task-parallel programs. Next, we present various data structures and synchronization primitives, including a barrier that requires the use of higher-order ghost state. Finally, we present a verified implementation of the DICE Protection Environment, an industry standard secure boot protocol. Taken together, our evaluation consists of more than 31,000 lines of verified code in a range of settings, providing evidence that PulseCore is both highly expressive as well as practical for a variety of program proof applications.

Data replication is crucial for enabling fault tolerance and uniform low latency in modern decentralized applications. Replicated Data Types (RDTs) have emerged as a principled approach for developing replicated implementations of basic data structures such as counter, flag, set, map, etc. While the correctness of RDTs is generally specified using the notion of strong eventual consistency–which guarantees that replicas that have received the same set of updates would converge to the same state–a more expressive specification which relates the converged state to updates received at a replica would be more beneficial to RDT users. Replication-aware linearizability is one such specification, which requires all replicas to always be in a state which can be obtained by linearizing the updates received at the replica. In this work, we develop a novel fully automated technique for verifying replication-aware linearizability for Mergeable Replicated Data Types (MRDTs). We identify novel algebraic properties for MRDT operations and the merge function which are sufficient for proving an implementation to be linearizable and which go beyond the standard notions of commutativity, associativity, and idempotence. We also develop a novel inductive technique called bottom-up linearization to automatically verify the required algebraic properties. Our technique can be used to verify both MRDTs and state-based CRDTs. We have successfully applied our approach to a number of complex MRDT and CRDT implementations including a novel JSON MRDT.

FastVer is a protocol that uses a variety of memory-checking techniques to monitor the integrity of key-value stores with only a modest runtime cost. Arasu et al. formalize the high-level design of FastVer in the F* proof assistant and prove it correct. However, their formalization did not yield a provably correct implementation---FastVer is implemented in unverified C++ code.

With an eye toward performance, interoperability, or legacy concerns, low-level system software often must parse binary encoded data formats. Few tools are available for this task, especially since the formats involve a mixture of arithmetic and data dependence, beyond what can be handled by typical parser generators. As such, parsers are written by hand in languages like C, with inevitable errors leading to security vulnerabilities.

We build a library SecFloat for secure 2-party computation (2PC) of 32-bit single-precision floating-point operations and math functions. The existing functionalities used in cryptographic works are imprecise and the precise functionalities used in standard libraries are not crypto-friendly, i.e., they use operations that are cheap on CPUs but have exorbitant cost in 2PC. SecFloat bridges this gap with its novel crypto-friendly precise functionalities. Compared to the prior cryptographic libraries, SecFloat is up to six orders of magnitude more precise and up to two orders of magnitude more efficient. Furthermore, against a precise 2PC baseline, SecFloat is three orders of magnitude more efficient. The high precision of SecFloat leads to the first accurate implementation of secure inference. All prior works on secure inference of deep neural networks rely on ad hoc float-to-fixed converters. We evaluate a model where the fixed-point approximations used in privacy-preserving machine learning completely fail and floating-point is necessary. Thus, emphasizing the need for libraries like SecFloat.

We present CELESTIAL, a framework for formally verifying smart contracts written in the Solidity language for the Ethereum blockchain. CELESTIAL allows programmers to write expressive functional specifications for their contracts. It translates the contracts and the specifications to F⋆ to formally verify, against an F⋆ model of the blockchain semantics, that the contracts meet their specifications. Once the verification succeeds, CELESTIAL performs an erasure of the specifications to generate Solidity code for execution on the Ethereum blockchain. We use CELESTIAL to verify several real-world smart contracts from different application domains. Our experience shows that CELESTIAL is a valuable tool for writing high-assurance smart contracts.

We present FastVer, a high-performance key-value store with strong data integrity guarantees. FastVer is built as an extension of FASTER, an open-source, high-performance key-value store. It offers the same key-value API as FASTER plus an additional verify() method that detects if an unauthorized attacker tampered with the database and checks whether results of all read operations are consistent with historical updates. FastVer is based on a novel approach that combines the advantages of Merkle trees and deferred memory verification. We show that this approach achieves one to two orders of magnitudes higher throughputs than traditional approaches based on either Merkle trees or memory verification. We have formally proven the correctness of our approach in a proof assistant, ensuring that verify() detects any inconsistencies, except if a collision can be found on a cryptographic hash.

Complex machine learning (ML) inference algorithms like recurrent neural networks (RNNs) use standard functions from math libraries like exponentiation, sigmoid, tanh, and reciprocal of square root. Although prior work on secure 2-party inference provides specialized protocols for convolutional neural networks (CNNs), existing secure implementations of these math operators rely on generic 2-party computation (2PC) protocols that suffer from high communication. We provide new specialized 2PC protocols for math functions that crucially rely on lookup-tables and mixed-bitwidths to address this performance overhead; our protocols for math functions communicate up to 423× less data than prior work. Furthermore, our math implementations are numerically precise, which ensures that the secure implementations preserve model accuracy of cleartext. We build on top of our novel protocols to build SiRnn, a library for end-to-end secure 2-party DNN inference, that provides the first secure implementations of an RNN operating on time series sensor data, an RNN operating on speech data, and a state-of-the-art ML architecture that combines CNNs and RNNs for identifying all heads present in images. Our evaluation shows that SiRnn achieves up to three orders of magnitude of performance improvement when compared to inference of these models using an existing state-of-the-art 2PC framework.

Steel is a language for developing and proving concurrent programs embedded in F ⋆ , a dependently typed programming language and proof assistant. Based on SteelCore, a concurrent separation logic (CSL) formalized in F ⋆ , our work focuses on exposing the proof rules of the logic in a form that enables programs and proofs to be effectively co-developed. Our main contributions include a new formulation of a Hoare logic of quintuples involving both separation logic and first-order logic, enabling efficient verification condition (VC) generation and proof discharge using a combination of tactics and SMT solving. We relate the VCs produced by our quintuple system to solving a system of associativity-commutativity (AC) unification constraints and develop tactics to (partially) solve these constraints using AC-matching modulo SMT-dischargeable equations. Our system is fully mechanized and implemented in F ⋆ . We evaluate it by developing several verified programs and libraries, including various sequential and concurrent linked data structures, proof libraries, and a library for 2-party session types. Our experience leads us to conclude that our system enables a mixture of automated and interactive proof, making it productive to build programs foundationally verified against a highly expressive, state-of-the-art CSL.

We present CrypTFlow2, a cryptographic framework for secure inference over realistic Deep Neural Networks (DNNs) using secure 2-party computation. CrypTFlow2 protocols are both correct -- i.e., their outputs are bitwise equivalent to the cleartext execution -- and efficient -- they outperform the state-of-the-art protocols in both latency and scale. At the core of CrypTFlow2, we have new 2PC protocols for secure comparison and division, designed carefully to balance round and communication complexity for secure inference tasks. Using CrypTFlow2, we present the first secure inference over ImageNet-scale DNNs like ResNet50 and DenseNet121. These DNNs are at least an order of magnitude larger than those considered in the prior work of 2-party DNN inference. Even on the benchmarks considered by prior work, CrypTFlow2 requires an order of magnitude less communication and 20x-30x less time than the state-of-the-art.

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. Even on MNIST/CIFAR, CrypTFlow outperforms prior work.

We present EverCrypt: a comprehensive collection of verified, high-performance cryptographic functionalities available via a carefully designed API. The API provably supports agility (choosing between multiple algorithms for the same functionality) and multiplexing (choosing between multiple implementations of the same algorithm). Through abstraction and zero-cost generic programming, we show how agility can simplify verification without sacrificing performance, and we demonstrate how C and assembly can be composed and verified against shared specifications. We substantiate the effectiveness of these techniques with new verified implementations (including hashes, Curve25519, and AES-GCM) whose performance matches or exceeds the best unverified implementations. We validate the API design with two high-performance verified case studies built atop EverCrypt, resulting in line-rate performance for a secure network protocol and a Merkle-tree library, used in a production blockchain, that supports 2.7 million insertions/sec. Altogether, EverCrypt consists of over 124K verified lines of specs, code, and proofs, and it produces over 29K lines of C and 14K lines of assembly code.

Much recent research has been devoted to modeling effects within type theory. Building on this work, we observe that effectful type theories can provide a foundation on which to build semantics for more complex programming constructs and program logics, extending the reasoning principles that apply within the host effectful type theory itself. Concretely, our main contribution is a semantics for concurrent separation logic (CSL) within the F ⋆ proof assistant in a manner that enables dependently typed, effectful F ⋆ programs to make use of concurrency and to be specified and verified using a full-featured, extensible CSL. In contrast to prior approaches, we directly derive the partial-correctness Hoare rules for CSL from the denotation of computations in the effectful semantics of non-deterministically interleaved atomic actions. Demonstrating the flexibility of our semantics, we build generic, verified libraries that support various concurrency constructs, ranging from dynamically allocated, storable spin locks, to protocol-indexed channels. We conclude that our effectful semantics provides a simple yet expressive basis on which to layer domain-specific languages and logics for verified, concurrent programming.

Building reliable applications for the cloud is challenging because of unpredictable failures during a program’s execution. This paper presents a programming framework, called Reliable State Machines (RSMs), that offers fault-tolerance by construction. In our framework, an application comprises several (possibly distributed) RSMs that communicate with each other via messages, much in the style of actor-based programming. Each RSM is fault-tolerant by design, thereby offering the illusion of being "always-alive". An RSM is guaranteed to process each input request exactly once, as one would expect in a failure-free environment. The RSM runtime automatically takes care of persisting state and rehydrating it on a failover. We present the core syntax and semantics of RSMs, along with a formal proof of failure-transparency. We provide a .NET implementation of the RSM framework for deploying services to Microsoft Azure. We carry out an extensive performance evaluation on micro-benchmarks to show that one can build high-throughput applications with RSMs. We also present a case study where we rewrite a significant part of a production cloud service using RSMs. The resulting service has simpler code and exhibits production-grade performance.

We introduce Meta-F $$^{\star }$$ , a tactics and metaprogramming framework for the F $$^\star $$ program verifier. The main novelty of Meta-F $$^\star $$ is allowing the use of tactics and metaprogramming to discharge assertions not solvable by SMT, or to just simplify them into well-behaved SMT fragments. Plus, Meta-F $$^\star $$ can be used to generate verified code automatically. Meta-F $$^\star $$ is implemented as an F $$^\star $$ effect, which, given the powerful effect system of F $$^{\star }$$ , heavily increases code reuse and even enables the lightweight verification of metaprograms. Metaprograms can be either interpreted, or compiled to efficient native code that can be dynamically loaded into the F $$^\star $$ type-checker and can interoperate with interpreted code. Evaluation on realistic case studies shows that Meta-F $$^\star $$ provides substantial gains in proof development, efficiency, and robustness.

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.

We present a compiler-based scheme to protect the confidentiality of sensitive data in low-level applications (e.g. those written in C) in the presence of an active adversary. In our scheme, the programmer marks sensitive data by lightweight annotations on the top-level definitions in the source code. The compiler then uses a combination of static dataflow analysis, runtime instrumentation, and a novel taint-aware form of control-flow integrity to prevent data leaks even in the presence of low-level attacks. To reduce runtime overheads, the compiler uses a novel memory layout.

High-performance cryptographic libraries often mix code written in a high-level language with code written in assembly. To support formally verifying the correctness and security of such hybrid programs, this paper presents an embedding of a subset of x64 assembly language in F* that allows efficient verification of both assembly and its interoperation with C code generated from F*. The key idea is to use the computational power of a dependent type system's type checker to run a verified verification-condition generator during type checking. This allows the embedding to customize the verification condition sent by the type checker to an SMT solver. By combining our proof-by-reflection style with SMT solving, we demonstrate improved automation for proving the correctness of assembly-language code. This approach has allowed us to complete the first-ever proof of correctness of an optimized implementation of AES-GCM, a cryptographic routine used by 90% of secure Internet traffic.

Relational properties describe multiple runs of one or more programs. They characterize many useful notions of security, program refinement, and equivalence for programs with diverse computational effects, and they have received much attention in the recent literature. Rather than developing separate tools for special classes of effects and relational properties, we advocate using a general purpose proof assistant as a unifying framework for the relational verification of effectful programs. The essence of our approach is to model effectful computations using monads and to prove relational properties on their monadic representations, making the most of existing support for reasoning about pure programs. We apply this method in F* and evaluate it by encoding a variety of relational program analyses, including information flow control, program equivalence and refinement at higher order, correctness of program optimizations and game-based cryptographic security. By relying on SMT-based automation, unary weakest preconditions, user-defined effects, and monadic reification, we show that, compared to unary properties, verifying relational properties requires little additional effort from the F* programmer.

We provide a way to ease the verification of programs whose state evolves monotonically. The main idea is that a property witnessed in a prior state can be soundly recalled in the current state, provided (1) state evolves according to a given preorder, and (2) the property is preserved by this preorder. In many scenarios, such monotonic reasoning yields concise modular proofs, saving the need for explicit program invariants. We distill our approach into the monotonic-state monad , a general yet compact interface for Hoare-style reasoning about monotonic state in a dependently typed language. We prove the soundness of the monotonic-state monad and use it as a unified foundation for reasoning about monotonic state in the F ⋆ verification system. Based on this foundation, we build libraries for various mutable data structures like monotonic references and apply these libraries at scale to the verification of several distributed applications.

Dijkstra monads enable a dependent type theory to be enhanced with support for specifying and verifying effectful code via weakest preconditions. Together with their closely related counterparts, Hoare monads, they provide the basis on which verification tools like F*, Hoare Type Theory (HTT), and Ynot are built. We show that Dijkstra monads can be derived "for free" by applying a continuation-passing style (CPS) translation to the standard monadic definitions of the underlying computational effects. Automatically deriving Dijkstra monads in this way provides a correct-by-construction and efficient way of reasoning about user-defined effects in dependent type theories. We demonstrate these ideas in EMF*, a new dependently typed calculus, validating it via both formal proof and a prototype implementation within F*. Besides equipping F* with a more uniform and extensible effect system, EMF* enables a novel mixture of intrinsic and extrinsic proofs within F*.

The record layer is the main bridge between TLS applications and internal sub-protocols. Its core functionality is an elaborate form of authenticated encryption: streams of messages for each sub-protocol (handshake, alert, and application data) are fragmented, multiplexed, and encrypted with optional padding to hide their lengths. Conversely, the sub-protocols may provide fresh keys or signal stream termination to the record layer. Compared to prior versions, TLS 1.3 discards obsolete schemes in favor of a common construction for Authenticated Encryption with Associated Data (AEAD), instantiated with algorithms such as AES-GCM and ChaCha20-Poly1305. It differs from TLS 1.2 in its use of padding, associated data and nonces. It also encrypts the content-type used to multiplex between sub-protocols. New protocol features such as early application data (0-RTT and 0.5-RTT) and late handshake messages require additional keys and a more general model of stateful encryption. We build and verify a reference implementation of the TLS record layer and its cryptographic algorithms in F*, a dependently typed language where security and functional guarantees can be specified as pre-and post-conditions. We reduce the high-level security of the record layer to cryptographic assumptions on its ciphers. Each step in the reduction is verified by typing an F* module, for each step that involves a cryptographic assumption, this module precisely captures the corresponding game. We first verify the functional correctness and injectivity properties of our implementations of one-time MAC algorithms (Poly1305 and GHASH) and provide a generic proof of their security given these two properties. We show the security of a generic AEAD construction built from any secure one-time MAC and PRF. We extend AEAD, first to stream encryption, then to length-hiding, multiplexed encryption. Finally, we build a security model of the record layer against an adversary that controls the TLS sub-protocols. We compute concrete security bounds for the AES_128_GCM, AES_256_GCM, and CHACHA20_POLY1305 ciphersuites, and derive recommended limits on sent data before re-keying. We plug our implementation of the record layer into the miTLS library, confirm that they interoperate with Chrome and Firefox, and report initial performance results. Combining our functional correctness, security, and experimental results, we conclude that the new TLS record layer (as described in RFCs and cryptographic standards) is provably secure, and we provide its first verified implementation.

We present Low*, a language for low-level programming and verification, and its application to high-assurance optimized cryptographic libraries. Low* is a shallow embedding of a small, sequential, well-behaved subset of C in F*, a dependently- typed variant of ML aimed at program verification. Departing from ML, Low* does not involve any garbage collection or implicit heap allocation; instead, it has a structured memory model à la CompCert, and it provides the control required for writing efficient low-level security-critical code. By virtue of typing, any Low* program is memory safe. In addition, the programmer can make full use of the verification power of F* to write high-level specifications and verify the functional correctness of Low* code using a combination of SMT automation and sophisticated manual proofs. At extraction time, specifications and proofs are erased, and the remaining code enjoys a predictable translation to C. We prove that this translation preserves semantics and side-channel resistance. We provide a new compiler back-end from Low* to C and, to evaluate our approach, we implement and verify various cryptographic algorithms, constructions, and tools for a total of about 28,000 lines of code. We show that our Low* code delivers performance competitive with existing (unverified) C cryptographic libraries, suggesting our approach may be applicable to larger-scale low-level software.

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-omega and even micro-F*, 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.

Current proposals for adding gradual typing to JavaScript, such as Closure, TypeScript and Dart, forgo soundness to deal with issues of scale, code reuse, and popular programming patterns. We show how to address these issues in practice while retaining soundness. We design and implement a new gradual type system, prototyped for expediency as a 'Safe' compilation mode for TypeScript. Our compiler achieves soundness by enforcing stricter static checks and embedding residual runtime checks in compiled code. It emits plain JavaScript that runs on stock virtual machines. Our main theorem is a simulation that ensures that the checks introduced by Safe TypeScript (1) catch any dynamic type error, and (2) do not alter the semantics of type-safe TypeScript code.

JavaScript's flexible semantics makes writing correct code hard and writing secure code extremely difficult. To address the former problem, various forms of gradual typing have been proposed, such as Closure and TypeScript. However, supporting all common programming idioms is not easy; for example, TypeScript deliberately gives up type soundness for programming convenience. In this paper, we propose a gradual type system and implementation techniques that provide important safety and security guarantees.

In a Secure Multiparty Computation (SMC), mutually distrusting parties use cryptographic techniques to cooperatively compute over their private data, in the process each party learns only explicitly revealed outputs. In this paper, we present Wysteria, a high-level programming language for writing SMCs. As with past languages, like Fairplay, Wysteria compiles secure computations to circuits that are executed by an underlying engine. Unlike past work, Wysteria provides support for mixed-mode programs, which combine local, private computations with synchronous SMCs. Wysteria complements a standard feature set with built-in support for secret shares and with wire bundles, a new abstraction that supports generic n-party computations. We have formalized Wysteria, its refinement type system, and its operational semantics. We show that Wysteria programs have an easy-to-understand single-threaded interpretation and prove that this view corresponds to the actual multi-threaded semantics. We also prove type soundness, a property we show has security ramifications, namely that information about one party's data can only be revealed to another via (agreed upon) secure computations. We have implemented Wysteria, and used it to program a variety of interesting SMC protocols from the literature, as well as several new ones. We find that Wysteria's performance is competitive with prior approaches while making programming far easier, and more trustworthy.

Gradual typing lets programmers evolve their dynamically typed programs by gradually adding explicit type annotations, which confer benefits like improved performance and fewer run-time failures.