Generating One-Hot Maps under Encryption

Fuente: arXiv
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Autori principali: Aharoni, Ehud, Drucker, Nir, Kushnir, Eyal, Masalha, Ramy, Shaul, Hayim
Natura: Preprint
Pubblicazione: 2023
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author Aharoni, Ehud
Drucker, Nir
Kushnir, Eyal
Masalha, Ramy
Shaul, Hayim
author_facet Aharoni, Ehud
Drucker, Nir
Kushnir, Eyal
Masalha, Ramy
Shaul, Hayim
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2306_06739
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Generating One-Hot Maps under Encryption
Aharoni, Ehud
Drucker, Nir
Kushnir, Eyal
Masalha, Ramy
Shaul, Hayim
Cryptography and Security
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.
title Generating One-Hot Maps under Encryption
topic Cryptography and Security
url https://arxiv.org/abs/2306.06739