One-hot maps are commonly used in the AI domain. Unsurprisingly, they can also bring great benefits to ML-based algorithms such as decision trees that run under Homomorphic Encryption (HE), specifically CKKS. Prior studies in this domain used these maps but assumed that the client encrypts them. Here, we consider different tradeoffs that may affect the client's decision on how to pack and store these maps. We suggest several conversion algorithms when working with encrypted data and report their costs. Our goal is to equip the ML over HE designer with the data it needs for implementing encrypted one-hot maps.

翻译：一热向量在人工智能领域中被广泛使用。毫不意外的是，它们也能为同态加密（HE）特别是CKKS方案下运行的决策树等基于机器学习的算法带来巨大优势。该领域的先前研究虽已使用这些向量，但均假设由客户端对其进行加密。本文考虑了可能影响客户端打包与存储策略的不同权衡方案，提出了若干适用于加密数据场景的转换算法，并报告了其计算开销。我们的目标是为基于HE的机器学习设计者提供实现加密一热向量所需的数据支持。