WTFHE: neural-netWork-ready Torus Fully Homomorphic Encryption

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00341864" target="_blank" >RIV/68407700:21230/20:00341864 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/20:00341864

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9134331" target="_blank" >https://ieeexplore.ieee.org/document/9134331</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/MECO49872.2020.9134331" target="_blank" >10.1109/MECO49872.2020.9134331</a>

Jazyk výsledku

angličtina

Název v původním jazyce

WTFHE: neural-netWork-ready Torus Fully Homomorphic Encryption

Popis výsledku v původním jazyce

We are currently witnessing two arising trends, which have a huge potential to threaten our privacy: the invasive sensors of the Internet of Things (IoT), and the powerful data mining techniques, in particular we focus on Neural Networks (NN's). For this reason, powerful countermeasures must be called for service: namely end-to-end encryption. Such an approach however requires an encryption scheme that enables processing of the encrypted data - this is known as the Fully Homomorphic Encryption (FHE). In this paper, we revisit an FHE scheme named TFHE, which is suitable for evaluation of NN's over encrypted input data, and we suggest to incorporate a verifiability feature to the evaluation process. Since there already exist other variants of the original TFHE scheme-currently only implemented in C++, which is rigid-we further introduce a library for rapid prototyping of new concepts related to TFHE. Our library is implemented in Ruby, which is an interpreted language and which goes with an interactive shell. Hence any new method can be speedily verified before implemented as a high-performance library.

Název v anglickém jazyce

WTFHE: neural-netWork-ready Torus Fully Homomorphic Encryption

Popis výsledku anglicky

We are currently witnessing two arising trends, which have a huge potential to threaten our privacy: the invasive sensors of the Internet of Things (IoT), and the powerful data mining techniques, in particular we focus on Neural Networks (NN's). For this reason, powerful countermeasures must be called for service: namely end-to-end encryption. Such an approach however requires an encryption scheme that enables processing of the encrypted data - this is known as the Fully Homomorphic Encryption (FHE). In this paper, we revisit an FHE scheme named TFHE, which is suitable for evaluation of NN's over encrypted input data, and we suggest to incorporate a verifiability feature to the evaluation process. Since there already exist other variants of the original TFHE scheme-currently only implemented in C++, which is rigid-we further introduce a library for rapid prototyping of new concepts related to TFHE. Our library is implemented in Ruby, which is an interpreted language and which goes with an interactive shell. Hence any new method can be speedily verified before implemented as a high-performance library.

Druh

D - Stať ve sborníku

CEP obor

—

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

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 9th Mediterranean Conference on Embedded Computing - MECO'2020

ISBN

978-1-7281-6949-1

ISSN

—

e-ISSN

2637-9511

Počet stran výsledku

5

Strana od-do

434-438

Název nakladatele

Institute of Electrical and Electronics Engineers, Inc.

Místo vydání

—

Místo konání akce

Budva

Datum konání akce

8. 6. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000612854100100