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A Pragmatic Introduction to Secure Multi-Party Computation
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TLDR
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 and provides a starting point for building applications using MPC and for developing MPC protocols, implementations, tools, and applications.Abstract:
Secure multi-party computation (MPC) has evolved from a theoretical curiosity in the 1980s to a tool for building real systems today. Over the past decade, MPC has been one of the most active resea...read more
Citations
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Journal ArticleDOI
Secure, privacy-preserving and federated machine learning in medical imaging
TL;DR: An overview of current and next-generation methods for federated, secure and privacy-preserving artificial intelligence with a focus on medical imaging applications, alongside potential attack vectors and future prospects in medical imaging and beyond are presented.
Posted Content
Robust Aggregation for Federated Learning
TL;DR: The experiments show that RFA is competitive with the classical aggregation when the level of corruption is low, while demonstrating greater robustness under high corruption, and establishes the convergence of the robust federated learning algorithm for the stochastic learning of additive models with least squares.
Journal ArticleDOI
End-to-end privacy preserving deep learning on multi-institutional medical imaging
Georgios Kaissis,Alexander Ziller,Jonathan Passerat-Palmbach,Théo Ryffel,Dmitrii Usynin,Andrew Trask,Ionésio Lima,Jason Mancuso,Friederike Jungmann,Marc-Matthias Steinborn,Andreas Saleh,Marcus R. Makowski,Daniel Rueckert,Daniel Rueckert,Rickmer Braren,Rickmer Braren +15 more
TL;DR: PriMIA (Privacy-preserving Medical Image Analysis), a free, open-source software framework for differentially private, securely aggregated federated learning and encrypted inference on medical imaging data, is presented.
Journal ArticleDOI
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
TL;DR: This article proposes the first secure aggregation framework, named Turbo-Aggregate, which employs a multi-group circular strategy for efficient model aggregation, and leverages additive secret sharing and novel coding techniques for injecting aggregation redundancy in order to handle user dropouts while guaranteeing user privacy.
Posted Content
Efficient Batched Oblivious PRF with Applications to Private Set Intersection.
TL;DR: In this article, Pinkas et al. describe a lightweight protocol for oblivious evaluation of a pseudorandom function (OPRF) in the presence of semihonest adversaries, which is particularly efficient when used to generate a large batch of OPRF instances.
References
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Journal ArticleDOI
How to share a secret
TL;DR: This technique enables the construction of robust key management schemes for cryptographic systems that can function securely and reliably even when misfortunes destroy half the pieces and security breaches expose all but one of the remaining pieces.
Book ChapterDOI
Public-key cryptosystems based on composite degree residuosity classes
TL;DR: A new trapdoor mechanism is proposed and three encryption schemes are derived : a trapdoor permutation and two homomorphic probabilistic encryption schemes computationally comparable to RSA, which are provably secure under appropriate assumptions in the standard model.
Proceedings ArticleDOI
Fully homomorphic encryption using ideal lattices
TL;DR: This work proposes a fully homomorphic encryption scheme that allows one to evaluate circuits over encrypted data without being able to decrypt, and describes a public key encryption scheme using ideal lattices that is almost bootstrappable.
Proceedings ArticleDOI
Random oracles are practical: a paradigm for designing efficient protocols
Mihir Bellare,Phillip Rogaway +1 more
TL;DR: It is argued that the random oracles model—where all parties have access to a public random oracle—provides a bridge between cryptographic theory and cryptographic practice, and yields protocols much more efficient than standard ones while retaining many of the advantages of provable security.
Book
The Algorithmic Foundations of Differential Privacy
Cynthia Dwork,Aaron Roth +1 more
TL;DR: The preponderance of this monograph is devoted to fundamental techniques for achieving differential privacy, and application of these techniques in creative combinations, using the query-release problem as an ongoing example.