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1. Fluid MPC: Secure Multiparty Computation with Dynamic Participants

   https://doi.org/10.1007/978-3-030-84245-1_4  

   Choudhuri, A.R. (August 2021, Advances in Cryptology, CRYPTO 2021)

   Malkin, T. (Ed.)

   Existing approaches to secure multiparty computation (MPC) require all participants to commit to the entire duration of the protocol. As interest in MPC continues to grow, it is inevitable that there will be a desire to use it to evaluate increasingly complex functionalities, resulting in computations spanning several hours or days. Such scenarios call for a dynamic participation model for MPC where participants have the flexibility to go offline as needed and (re)join when they have available computational resources. Such a model would also democratize access to privacy-preserving computation by facilitating an “MPC-as-a-service” paradigm—the deployment of MPC in volunteer-operated networks (such as blockchains, where dynamism is inherent) that perform computation on behalf of clients. In this work, we initiate the study of fluid MPC, where parties can dynamically join and leave the computation. The minimum commitment required from each participant is referred to as fluidity, measured in the number of rounds of communication that it must stay online. Our contributions are threefold: We provide a formal treatment of fluid MPC, exploring various possible modeling choices. We construct information-theoretic fluid MPC protocols in the honest-majority setting. Our protocols achieve maximal fluidity, meaning that a party can exit the computation after receiving and sending messages in one round. We implement our protocol and test it in multiple network settings. 

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2. RPM: Robust Anonymity at Scale

   https://doi.org/10.56553/POPETS-2023-0057  

   Lu, Donghang; Kate, Aniket (April 2023, Proceedings on Privacy Enhancing Technologies)

   Mazurek, Michelle L; Sherr, Micah. (Ed.)

   This work presents RPM, a scalable anonymous communication protocol suite using secure multiparty computation (MPC) with the offline-online model. We generate random, unknown permutation matrices in a secret-shared fashion and achieve improved (online) performance and the lightest communication and computation overhead for the clients compared to the state of art robust anonymous communication protocols. Using square-lattice shuffling, we make our protocol scale well as the number of clients increases. We provide three protocol variants, each targeting different input volumes and MPC frameworks/libraries. Besides, due to the modular design, our protocols can be easily generalized to support more MPC functionalities and security properties as they get developed. We also illustrate how to generalize our protocols to support two-way anonymous communication and secure sorting. We have implemented our protocols using the MP-SPDZ library suit and the benchmark illustrates that our protocols achieve unprecedented online phase performance with practical offline phases. 

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3. The More The Merrier: Reducing the Cost of Large Scale MPC

   Gordon, S. Dov; Starin, Daniel; Yerukhimovich, Arkady (January 2021, Eurocrypt)

   Canteaut, Anne; Standaert, Francois-Xavier (Ed.)

   Secure multi-party computation (MPC) allows multiple par-ties to perform secure joint computations on their private inputs. To-day, applications for MPC are growing with thousands of parties wish-ing to build federated machine learning models or trusted setups for blockchains. To address such scenarios we propose a suite of novel MPC protocols that maximize throughput when run with large numbers of parties. In particular, our protocols have both communication and computation complexity that decrease with the number of parties. Our protocols build on prior protocols based on packed secret-sharing, introducing new techniques to build more efficient computation for general circuits. Specifically, we introduce a new approach for handling linear attacks that arise in protocols using packed secret-sharing and we propose a method for unpacking shared multiplication triples without increasingthe asymptotic costs. Compared with prior work, we avoid the log|C|overhead required when generically compiling circuits of size |C| for use in a SIMD computation, and we improve over folklore “committee-based” solutions by a factor of O(s), the statistical security parameter. In practice, our protocol is up to 10X faster than any known construction, under a reasonable set of parameters. 

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4. The More The Merrier: Reducing the Cost of Large Scale MPC

   Gordon, S. Dov; Starin, Daniel; Yerukhimovich, Arkady (January 2021, Eurocrypt)

   Canteaut, Anne; Standaert, Francois-Xavier (Ed.)

   Secure multi-party computation (MPC) allows multiple par-ties to perform secure joint computations on their private inputs. To-day, applications for MPC are growing with thousands of parties wish-ing to build federated machine learning models or trusted setups for blockchains. To address such scenarios we propose a suite of novel MPC protocols that maximize throughput when run with large numbers of parties. In particular, our protocols have both communication and computation complexity that decrease with the number of parties. Our protocols build on prior protocols based on packed secret-sharing, introducing new techniques to build more efficient computation for general circuits. Specifically, we introduce a new approach for handling linear attacks that arise in protocols using packed secret-sharing and we propose a method for unpacking shared multiplication triples without increasingthe asymptotic costs. Compared with prior work, we avoid the log|C|overhead required when generically compiling circuits of size |C| for use in a SIMD computation, and we improve over folklore “committee-based” solutions by a factor of O(s), the statistical security parameter. In practice, our protocol is up to 10X faster than any known construction, under a reasonable set of parameters. 

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5. CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU

   https://doi.org/10.1109/SP40001.2021.00098  

   Tan, Sijun; Knott, Brian; Tian, Yuan; Wu, David J. (May 2021, IEEE Symposium on Security and Privacy)

   We introduce CryptGPU, a system for privacy-preserving machine learning that implements all operations on the GPU (graphics processing unit). Just as GPUs played a pivotal role in the success of modern deep learning, they are also essential for realizing scalable privacy-preserving deep learning. In this work, we start by introducing a new interface to losslessly embed cryptographic operations over secret-shared values (in a discrete domain) into floating-point operations that can be processed by highly-optimized CUDA kernels for linear algebra. We then identify a sequence of “GPU-friendly” cryptographic protocols to enable privacy-preserving evaluation of both linear and non-linear operations on the GPU. Our microbenchmarks indicate that our private GPU-based convolution protocol is over 150x faster than the analogous CPU-based protocol; for non-linear operations like the ReLU activation function, our GPU-based protocol is around 10x faster than its CPU analog. With CryptGPU, we support private inference and private training on convolutional neural networks with over 60 million parameters as well as handle large datasets like ImageNet. Compared to the previous state-of-the-art, when considering large models and datasets, our protocols achieve a 2x to 8x improvement in private inference and a 6x to 36x improvement for private training. Our work not only showcases the viability of performing secure multiparty computation (MPC) entirely on the GPU to enable fast privacy-preserving machine learning, but also highlights the importance of designing new MPC primitives that can take full advantage of the GPU's computing capabilities. 

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