Single-Trace Attack on NTRU Decryption with Machine Learning and Template Profiling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F23%3A00368879" target="_blank" >RIV/68407700:21240/23:00368879 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/DSD60849.2023.00027" target="_blank" >https://doi.org/10.1109/DSD60849.2023.00027</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/DSD60849.2023.00027" target="_blank" >10.1109/DSD60849.2023.00027</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Single-Trace Attack on NTRU Decryption with Machine Learning and Template Profiling

Popis výsledku v původním jazyce

NTRU is a post-quantum public key cryptosystem based on lattices and was a finalist in the 3rd round of the post-quantum standardization process organized by the National Institute of Standards and Technology (NIST). We present a new single-trace attack against the NTRU decryption algorithm, meaning we can obtain the private key only from one power trace, assuming the profiling phase was conducted before the attack. The attack was performed on the submission implementation from the standardization process. We used several machine learning methods and Template attacks for the profiling and testing phase. Both methods achieve high accuracy reaching almost 100% tested on more than 12 000 traces. We also provide a comparison of their success score with regard to a different number of points of interest

Název v anglickém jazyce

Single-Trace Attack on NTRU Decryption with Machine Learning and Template Profiling

Popis výsledku anglicky

NTRU is a post-quantum public key cryptosystem based on lattices and was a finalist in the 3rd round of the post-quantum standardization process organized by the National Institute of Standards and Technology (NIST). We present a new single-trace attack against the NTRU decryption algorithm, meaning we can obtain the private key only from one power trace, assuming the profiling phase was conducted before the attack. The attack was performed on the submission implementation from the standardization process. We used several machine learning methods and Template attacks for the profiling and testing phase. Both methods achieve high accuracy reaching almost 100% tested on more than 12 000 traces. We also provide a comparison of their success score with regard to a different number of points of interest

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

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

2023 26th Euromicro Conference on Digital System Design (DSD)

ISBN

979-8-3503-4419-6

ISSN

2771-2508

e-ISSN

2771-2508

Počet stran výsledku

6

Strana od-do

124-129

Název nakladatele

IEEE Computer Society

Místo vydání

Los Alamitos

Místo konání akce

Durres

Datum konání akce

6. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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