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.

翻译：匹配机制已成为当代应用中的重要组成部分，被广泛应用于约会软件、社交媒体、在线游戏、接触者追踪及其他多种场景。然而，大多数匹配机制的实现需要收集敏感或个人数据才能正常运行。为此，本研究旨在降低匹配应用中固有的隐私泄露风险。我们提出H-Elo——一种基于全同态加密的私密评分系统，通过使用传统评分系统实现安全匹配。本文介绍了H-Elo的构建方案，分析了其在面对强大对手时的安全性，并在基于国际象棋的评分更新场景中对系统性能进行了基准测试。实验表明，H-Elo在保持评分值私密且安全的同时，能够达到与明文实现相近的准确度。此外，我们将本研究与其他私密匹配方案进行了比较，并探讨了私密匹配领域未来的研究方向。据我们所知，本研究首次提出了基于评分系统的私密安全匹配协议。