Differentially private stochastic gradient descent (DP-SGD) trains machine learning (ML) models with formal privacy guarantees for the training set by adding random noise to gradient updates. In collaborative learning (CL), where multiple parties jointly train a model, noise addition occurs either (i) before or (ii) during secure gradient aggregation. The first option is deployed in distributed DP methods, which require greater amounts of total noise to achieve security, resulting in degraded model utility. The second approach preserves model utility but requires a secure multiparty computation (MPC) protocol. Existing methods for MPC noise generation require tens to hundreds of seconds of runtime per noise sample because of the number of parties involved. This makes them impractical for collaborative learning, which often requires thousands or more samples of noise in each training step.

Secure multi-party computation (MPC) is a crucial tool for privacy-preserving computation, but it is getting in-creasingly complicated due to recent advancements and opti-mizations. Programming tools for MPC allow programmers to develop MPC applications without mastering all cryptography. However, most existing MPC programming tools fail to attract real users due to the lack of documentation, maintenance, and the ability to compose with legacy codebases. In this work, we build Smaug, a modular extension of LLVM. Smaug seamlessly brings all LLVM support to MPC programmers, including error messaging, documentation, code optimization, and frontend support to compile from various languages to LLVM intermediate representation (IR). Smaug can efficiently convert non-oblivious LLVM IR to their oblivious counterparts while applying popular optimizations as LLVM code trans-formations. With benchmarks written in C++ and Rust and backends for Yao and GMW protocols, we observe that Smaug performs as well as (and sometimes much better than) prior tools using domain-specific languages with similar backends. Finally, we use Smaug to compile open-source projects that implement Minesweeper and Blackjack, producing usable two-party games with ease.

Protocols for secure multi-party computation (MPC) supporting mixed-mode computation have found a lot of applications in recent years due to their flexibility in representing the function to be evaluated. However, existing mixed-mode MPC protocols are only practical for a small number of parties: they are either tailored to the case of two/three parties, or scale poorly for a large number of parties.In this paper, we design and implement a new system for highly efficient and scalable mixed-mode MPC tolerating an arbitrary number of semi-honest corruptions. Our protocols allow secret data to be represented in Encrypted, Boolean, Arithmetic, or Yao form, and support efficient conversions between these representations.1)We design a multi-party table-lookup protocol, where both the index and the table can be kept private. The protocol is scalable even with hundreds of parties.2)Using the above protocol, we design efficient conversions between additive arithmetic secret sharings and Boolean secret sharings for a large number of parties. For 32 parties, our conversion protocols require 1184× to 8141× less communication compared to the state-of-the-art protocols MOTION and MP-SPDZ; this leads to up to 1275× improvement in running time under 1 Gbps network. The improvements are even larger with more parties.3)We also use new protocols to design an efficient multi-party distributed garbling protocol. The protocol could achieve asymptotically constant communication per party.Our implementation will be made public.