Language-based Techniques for Practical and Trustworthy Secure Multi-party Computations

Secure Multi-party Computation (MPC) enables a set of parties to collaboratively compute, using cryptographic protocols, a function over their private data in a way that the participants do not see each other's data, they only see the final output. Typical MPC examples include statistical computations over joint private data, private set intersection, and auctions. While these applications are examples of monolithic MPC, richer MPC applications move between "normal" (i.e., per-party local) and "secure" (i.e., joint, multi-party secure) modes repeatedly, resulting overall in mixed-mode computations. For example, we might use MPC to implement the role of the dealer in a game of mental poker -- the game will be divided into rounds of local decision-making (e.g. bidding) and joint interaction (e.g. dealing). Mixed-mode computations are also used to improve performance over monolithic secure computations. Starting with the Fairplay project, several MPC frameworks have been proposed in the last decade to help programmers write MPC applications in a high-level language, while the toolchain manages the low-level details. However, these frameworks are either not expressive enough to allow writing mixed-mode applications or lack formal specification, and reasoning capabilities, thereby diminishing the parties' trust in such tools, and the programs written using them. Furthermore, none of the frameworks provides a verified toolchain to run the MPC programs, leaving the potential of security holes that can compromise the privacy of parties' data. This dissertation presents language-based techniques to make MPC more practical and trustworthy. First, it presents the design and implementation of a new MPC Domain Specific Language, called Wysteria, for writing rich mixed-mode MPC applications. Wysteria provides several benefits over previous languages, including a conceptual single thread of control, generic support for more than two parties, high-level abstractions for secret shares, and a fully formalized type system and operational semantics. Using Wysteria, we have implemented several MPC applications, including, for the first time, a card dealing application. The dissertation next presents Wys*, an embedding of Wysteria in F*, a full-featured verification oriented programming language. Wys* improves on Wysteria along three lines: (a) It enables programmers to formally verify the correctness and security properties of their programs. As far as we know, Wys* is the first language to provide verification capabilities for MPC programs. (b) It provides a partially verified toolchain to run MPC programs, and finally (c) It enables the MPC programs to use, with no extra effort, standard language constructs from the host language F*, thereby making it more usable and scalable. Finally, the dissertation develops static analyses that help optimize monolithic MPC programs into mixed-mode MPC programs, while providing similar privacy guarantees as the monolithic versions

SteelCore: An extensible concurrent separation logic for effectful dependently typed programs

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.Fil: Swamy, Nikhil. Microsoft Research; Estados UnidosFil: Rastogi, Aseem. Microsoft Research; IndiaFil: Fromherz, Aymeric. University of Carnegie Mellon; Estados UnidosFil: Merigoux, Denis. Institut National de Recherche en Informatique et en Automatique; FranciaFil: Ahman, Danel. University of Ljubljana; EsloveniaFil: Martínez, Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentin

Meta-F*: Proof Automation with SMT, Tactics, and Metaprograms

We introduce Meta-F*, a tactics and metaprogramming framework for the F* program verifier. The main novelty of Meta-F* 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* can be used to generate verified code automatically. Meta-F* is implemented as an F* effect, which, given the powerful effect system of F*, 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* type-checker and can interoperate with interpreted code. Evaluation on realistic case studies shows that Meta-F* provides substantial gains in proof development, efficiency, and robustness.Comment: Full version of ESOP'19 pape

EzPC: Programmable, Efficient, and Scalable Secure Two-Party Computation for Machine Learning

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 sharing and garbled circuits for better performance. We empirically demonstrate that the protocols generated by our framework match or outperform (up to 19x) recent works that provide hand-crafted protocols for various functionalities such as secure prediction and matrix factorization

Reliable State Machines: A Framework for Programming Reliable Cloud Services

Building reliable applications for the cloud is challenging because of unpredictable failures during a program\u27s 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

Ranking LLM-Generated Loop Invariants for Program Verification

Synthesizing inductive loop invariants is fundamental to automating program verification. In this work, we observe that Large Language Models (such as gpt-3.5 or gpt-4) are capable of synthesizing loop invariants for a class of programs in a 0-shot setting, yet require several samples to generate the correct invariants. This can lead to a large number of calls to a program verifier to establish an invariant. To address this issue, we propose a {\it re-ranking} approach for the generated results of LLMs. We have designed a ranker that can distinguish between correct inductive invariants and incorrect attempts based on the problem definition. The ranker is optimized as a contrastive ranker. Experimental results demonstrate that this re-ranking mechanism significantly improves the ranking of correct invariants among the generated candidates, leading to a notable reduction in the number of calls to a verifier.Comment: Findings of The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP-findings 2023