Large-Scale MPC: Scaling Private Iris Code Uniqueness Checks to Millions of Users
Remco Bloemen, Bryan Gillespie, Daniel Kales, Philipp Sippl, and Roman Walch

TL;DR
This paper presents a highly efficient secure multiparty computation protocol for large-scale iris code comparisons, enabling privacy-preserving biometric verification at unprecedented speeds suitable for real-world deployment.
Contribution
It introduces novel MPC protocols that significantly outperform previous systems, achieving over 4 billion comparisons per second on GPU clusters for privacy-preserving iris verification.
Findings
Over 690,000 comparisons/sec on a single CPU core
4.29 billion comparisons/sec on GPU clusters
Protocols meet real-world deployment performance requirements
Abstract
In this work we tackle privacy concerns in biometric verification systems that typically require server-side processing of sensitive data (e.g., fingerprints and Iris Codes). Concretely, we design a solution that allows us to query whether a given Iris Code is similar to one contained in a given database, while all queries and datasets are being protected using secure multiparty computation (MPC). Addressing the substantial performance demands of operational systems like World ID and aid distributions by the Red Cross, we propose new protocols to improve performance by more than three orders of magnitude compared to the recent state-of-the-art system Janus (S&P 24). Our final protocol can achieve a throughput of over 690 thousand Iris Code comparisons per second on a single CPU core, while protecting the privacy of both the query and database Iris Codes. Furthermore, using Nvidia NCCL…
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Taxonomy
TopicsAdvanced Data Storage Technologies · Cellular Automata and Applications · Algorithms and Data Compression
