TFHE Parameter Setup for Effective and Error-Free Neural Network Prediction on Encrypted Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00350548" target="_blank" >RIV/68407700:21230/21:00350548 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-030-80129-8_49" target="_blank" >https://doi.org/10.1007/978-3-030-80129-8_49</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-80129-8_49" target="_blank" >10.1007/978-3-030-80129-8_49</a>

Result language

angličtina

Original language name

TFHE Parameter Setup for Effective and Error-Free Neural Network Prediction on Encrypted Data

Original language description

With the rise of Cloud and Big Data technologies, Machine Learning as a Service (MLaaS) receives much attention, too. However, in some sense, the current situation resembles the era of wild digitalization, where security was pushed to the sideline. Back then, the problem was mostly about misunderstanding of severe consequences that insecure digitalization might bring. To date, common awareness of security has improved significantly, however, currently we are facing rather a technological challenge. Indeed, we are still missing a competitive and satisfactory solution that would secure MLaaS. In this paper, we contribute to the very recent line of research, which utilizes a Fully Homomorphic Encryption (FHE) scheme by Chillotti et al. named TFHE. It has been shown that TFHE is particularly suitable for securing MLaaS. In addition, its security relies on the famous LWE problem, which is considered quantum-proof. However, it has not been studied yet how all the TFHE parameters are to be set. Hence we provide a thorough analysis of error propagation through TFHE homomorphic computations, based on which we derive constraints on the parameters as well as we suggest a convenient representation of internal objects. We particularly focus on effective resource utilization in order to achieve the best performance of any prospective implementation.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Intelligent Computing

ISBN

978-3-030-80128-1

ISSN

2367-3370

e-ISSN

2367-3389

Number of pages

20

Pages from-to

702-721

Publisher name

Springer Nature Switzerland AG

Place of publication

Basel

Event location

London

Event date

Jul 15, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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