Generating One-Hot Maps under Encryption

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. Her...

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Bibliographic Details
Published inarXiv.org
Main Authors Aharoni, Ehud, Drucker, Nir, Kushnir, Eyal, Masalha, Ramy, Shaul, Hayim
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 11.06.2023
Subjects
Algorithms
Computer Science - Cryptography and Security
Decision trees
Domains
Encryption
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2306.06739