Hidden Elo: Private Matchmaking through Encrypted Rating Systems

2026-03-27

AI Summary

This work proposes H-Elo, the first private Elo rating and matching system based on fully homomorphic encryption (FHE), which enables secure rating updates and matchmaking entirely in the encrypted domain. Traditional matching systems rely on users’ sensitive rating data, posing significant privacy risks; in contrast, H-Elo performs all matching computations without exposing raw ratings, thereby offering strong privacy guarantees. Experimental evaluation in a chess-playing scenario demonstrates that H-Elo achieves matching accuracy comparable to that of plaintext systems while effectively preserving user privacy. To the best of our knowledge, this is the first encrypted matching protocol that simultaneously attains high accuracy and robust privacy protection.

Technology Category

• Machine Learning: Privacy
• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design

Application Category

• Security and Privacy: Privacy-enhancing technologies
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Matchmaking has become a prevalent part in contemporary applications, being used in dating apps, social media, online games, contact tracing and in various other use-cases. However, most implementations of matchmaking require the collection of sensitive/personal data for proper functionality. As such, with this work we aim to reduce the privacy leakage inherent in matchmaking applications. We propose H-Elo, a Fully Homomorphic Encryption (FHE)-based, private rating system, which allows for secure matchmaking through the use of traditional rating systems. In this work, we provide the construction of H-Elo, analyse the security of it against a capable adversary as well as benchmark our construction in a chess-based rating update scenario. Through our experiments we show that H-Elo can achieve similar accuracy to a plaintext implementation, while keeping rating values private and secure. Additionally, we compare our work to other private matchmaking solutions as well as cover some future directions in the field of private matchmaking. To the best of our knowledge we provide one of the first private and secure rating system-based matchmaking protocols.

Problem

• matchmaking
• privacy leakage
• rating systems
• sensitive data
• private matchmaking

Innovation

• Fully Homomorphic Encryption
• Private Matchmaking
• Encrypted Rating System
• H-Elo
• Privacy-Preserving Computation