A Pragmatic Introduction to Secure Multi-Party Computation

By David E. Evans, Vladimir Kolesnikov, Mike Rosulek. 2019

499 citations recorded by the source corpus.

Since its introduction by Andrew Yao in the 1980s, multi-party computation has developed from a theoretical curiosity to an important tool for building large-scale privacy-preserving applications. Secure multi-party computation (MPC) enables a group to jointly perform a computation without disclosing any participant’s private inputs. The participants agree on a function to compute, and then can use an MPC protocol to jointly compute the output of that function on their secret inputs without revealing them. This monograph provides an introduction to multi-party computation for practitioners interested in building privacy-preserving applications and researchers who want to work in the area. The authors introduce the foundations of MPC and describe the current state of the art. The goal is to enable readers to understand what is possible today, and what may be possible in the future. It provides a starting point for building applications using MPC and for developing MPC protocols, implementations, tools, and applications. Those seeking a concise, accessible introduction to the topic which quickly enables them to build practical systems or conduct further research will find this essential reading.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

Citations in the corpus

DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks (2020, methodology): “Additionally, techniques such as multi-party computation [14, 15] and differential privacy [16, 17, 18, 19] have been employed to improve the privacy in federated-learning.”
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning (2020, background): “The second key component is to leverage additive secret sharing [12], [13] to enable privacy and security of the users.”
Differentially-Private Multi-Party Sketching for Large-Scale Statistics (2020, background): “Recently, a large amount of active research has been conducted toward achieving efficient protocols for secure MPC (see [31] and the references therein).”
PrivEdge: From Local to Distributed Private Training and Prediction (2020, methodology): “We refer the reader to [73] for a detailed presentation and security analysis of Garbled Circuits.”
Trustworthy AI: From Principles to Practices (2021, background): “A typical SMPC protocol satisfies properties of privacy, correctness, independence of inputs, guaranteed output delivery, and fairness [114, 387].”
CrypTen: Secure Multi-Party Computation Meets Machine Learning (2021, background): “Secure multi-party computation (MPC; [30, 69]) allows parties to collaboratively perform computations on their combined data sets without revealing the data they possess to each other.”
Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity (2022, background): “From a computer science standpoint, the ideal tool to use here is secure multiparty computation [27], which allows two parties to perform a joint calculation based on hidden data while learning no more than (what can be inferred from) the result.”
Secure, privacy-preserving and federated machine learning in medical imaging (2020, unknown): “74 .”