  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.
