Cheetah: Lean and fast secure {Two-Party} deep neural network inference
Secure two-party neural network inference (2PC-NN) can offer privacy protection for both the
client and the server and is a promising technique in the machine-learning-as-a-service …
client and the server and is a promising technique in the machine-learning-as-a-service …
Towards practical secure neural network inference: the journey so far and the road ahead
Neural networks (NNs) have become one of the most important tools for artificial
intelligence. Well-designed and trained NNs can perform inference (eg, make decisions or …
intelligence. Well-designed and trained NNs can perform inference (eg, make decisions or …
Indistinguishability obfuscation from well-founded assumptions
Indistinguishability obfuscation, introduced by [Barak et. al. Crypto 2001], aims to compile
programs into unintelligible ones while preserving functionality. It is a fascinating and …
programs into unintelligible ones while preserving functionality. It is a fascinating and …
{ABY2. 0}: Improved {Mixed-Protocol} secure {Two-Party} computation
Secure Multi-party Computation (MPC) allows a set of mutually distrusting parties to jointly
evaluate a function on their private inputs while maintaining input privacy. In this work, we …
evaluate a function on their private inputs while maintaining input privacy. In this work, we …
Privacy-Preserving Data-Driven Learning Models for Emerging Communication Networks: A Comprehensive Survey
With the proliferation of Beyond 5G (B5G) communication systems and heterogeneous
networks, mobile broadband users are generating massive volumes of data that undergo …
networks, mobile broadband users are generating massive volumes of data that undergo …
Elsa: Secure aggregation for federated learning with malicious actors
Federated learning (FL) is an increasingly popular approach for machine learning (ML) in
cases where the training dataset is highly distributed. Clients perform local training on their …
cases where the training dataset is highly distributed. Clients perform local training on their …
Wolverine: fast, scalable, and communication-efficient zero-knowledge proofs for boolean and arithmetic circuits
Efficient zero-knowledge (ZK) proofs for arbitrary boolean or arithmetic circuits have recently
attracted much attention. Existing solutions suffer from either significant prover overhead (ie …
attracted much attention. Existing solutions suffer from either significant prover overhead (ie …
Ferret: Fast extension for correlated OT with small communication
Correlated oblivious transfer (COT) is a crucial building block for secure multi-party
computation (MPC) and can be generated efficiently via OT extension. Recent works based …
computation (MPC) and can be generated efficiently via OT extension. Recent works based …
Mystique: Efficient conversions for {Zero-Knowledge} proofs with applications to machine learning
Recent progress in interactive zero-knowledge (ZK) proofs has improved the efficiency of
proving large-scale computations significantly. Nevertheless, real-life applications (eg, in the …
proving large-scale computations significantly. Nevertheless, real-life applications (eg, in the …
Quicksilver: Efficient and affordable zero-knowledge proofs for circuits and polynomials over any field
Zero-knowledge (ZK) proofs with an optimal memory footprint have attracted a lot of
attention, because such protocols can easily prove very large computation with a small …
attention, because such protocols can easily prove very large computation with a small …