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1. 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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2. Secret Sharing MPC on FPGAs in the Datacenter

   Wolfe, Pierre-Francois; Patel, Rushi; Munafo, Robert; Varia, Mayank; Herbordt, Martin (August 2020, International Conference on Field Programmable Logic and Applications)

   Multi-Party Computation (MPC) is a technique enabling data from several sources to be used in a secure computation revealing only the result while protecting the original data, facilitating shared utilization of data sets gathered by different entities. The presence of Field Programmable Gate Array (FPGA) hardware in datacenters can provide accelerated computing as well as low latency, high bandwidth communication that bolsters the performance of MPC and lowers the barrier to using MPC for many applications. In this work, we propose a Secret Sharing FPGA design based on the protocol described by Araki et al. We compare our hardware design to the original authors' software implementations of Secret Sharing and to work accelerating MPC protocols based on Garbled Circuits with FPGAs. Our conclusion is that Secret Sharing in the datacenter is competitive and when implemented on FPGA hardware was able to use at least 10x fewer computer resources than the original work using CPUs. 

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3. Conclave: secure multi-party computation on big data

   https://doi.org/10.1145/3302424.3303982  

   Volgushev, Nikolaj; Schwarzkopf, Malte; Getchell, Ben; Varia, Mayank; Lapets, Andrei; Bestavros, Azer (March 2019, Fourteenth EuroSys Conference 2019)

   Secure Multi-Party Computation (MPC) allows mutually distrusting parties to run joint computations without revealing private data. Current MPC algorithms scale poorly with data size, which makes MPC on "big data" prohibitively slow and inhibits its practical use. Many relational analytics queries can maintain MPC's end-to-end security guarantee without using cryptographic MPC techniques for all operations. Conclave is a query compiler that accelerates such queries by transforming them into a combination of data-parallel, local cleartext processing and small MPC steps. When parties trust others with specific subsets of the data, Conclave applies new hybrid MPC-cleartext protocols to run additional steps outside of MPC and improve scalability further. Our Conclave prototype generates code for cleartext processing in Python and Spark, and for secure MPC using the Sharemind and Obliv-C frameworks. Conclave scales to data sets between three and six orders of magnitude larger than state-of-the-art MPC frameworks support on their own. Thanks to its hybrid protocols, Conclave also substantially outperforms SMCQL, the most similar existing system. 

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4. Delegated Private Matching For Compute

   https://doi.org/10.56553/popets-2024-0040  

   Mouris, Dimitris; Masny, Daniel; Trieu, Ni; Sengupta, Shubho; Buddhavarapu, Prasad; Case, Benjamin (April 2024, Proceedings on Privacy Enhancing Technologies)

   Private matching for compute (PMC) establishes a match between two datasets owned by mutually distrusted parties (C and P) and allows the parties to input more data for the matched records for arbitrary downstream secure computation without rerunning the private matching component. The state-of-the-art PMC protocols only support two parties and assume that both parties can participate in computationally intensive secure computation. We observe that such operational overhead limits the adoption of these protocols to solely powerful entities as small data owners or devices with minimal computing power will not be able to participate. We introduce two protocols to delegate PMC from party P to untrusted cloud servers, called delegates, allowing multiple smaller P parties to provide inputs containing identifiers and associated values. Our Delegated Private Matching for Compute protocols, called DPMC and DsPMC, establish a join between the datasets of party C and multiple delegators P based on multiple identifiers and compute secret shares of associated values for the identifiers that the parties have in common. We introduce a rerandomizable encrypted oblivious pseudorandom function (OPRF) primitive, called EO, which allows two parties to encrypt, mask, and shuffle their data. Note that EO may be of independent interest. Our DsPMC protocol limits the leakages of DPMC by combining our EO scheme and secure three-party shuffling. Finally, our implementation demonstrates the efficiency of our constructions by outperforming related works by approximately 10x for the total protocol execution and by at least 20x for the computation on the delegators. 

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5. Multi-Party Replicated Secret Sharing over a Ring with Applications to Privacy-Preserving Machine Learning

   https://doi.org/10.56553/popets-2023-0035  

   Baccarini, Alessandro; Blanton, Marina; Yuan, Chen (January 2023, Proceedings on Privacy Enhancing Technologies)

   Secure multi-party computation has seen significant performance advances and increasing use in recent years. Techniques based on secret sharing offer attractive performance and are a popular choice for privacy-preserving machine learning applications. Traditional techniques operate over a field, while designing equivalent techniques for a ring Z_2^k can boost performance. In this work, we develop a suite of multi-party protocols for a ring in the honest majority setting starting from elementary operations to more complex with the goal of supporting general-purpose computation. We demonstrate that our techniques are substantially faster than their field-based equivalents when instantiated with a different number of parties and perform on par with or better than state-of-the-art techniques with designs customized for a fixed number of parties. We evaluate our techniques on machine learning applications and show that they offer attractive performance. 

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