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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Intelligent Computing

ISBN

978-3-030-80128-1

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

20

Strana od-do

702-721

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Basel

Místo konání akce

London

Datum konání akce

15. 7. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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