Towards Private Deep Learning-Based Side-Channel Analysis Using Homomorphic Encryption Opportunities and Limitations

被引：2

作者：

Schmid, Fabian ^{[1
]}

Mukherjee, Shibam ^{[1
,5
]}

Picek, Stjepan ^{[2
]}

Stoettinger, Marc ^{[3
]}

De Santis, Fabrizio ^{[4
]}

Rechberger, Christian ^{[1
]}

机构：

[1] Graz Univ Technol, Graz, Austria

[2] Radboud Univ Nijmegen, Nijmegen, Netherlands

[3] RheinMain Univ Appl Sci, Wiesbaden, Germany

[4] Siemens AG, Munich, Germany

[5] Know Ctr GmbH, Graz, Austria

来源：

CONSTRUCTIVE SIDE-CHANNEL ANALYSIS AND SECURE DESIGN, COSADE 2024 | 2024年 / 14595卷

关键词：

Side-channel Analysis; Deep Learning; Neural Networks; Homomorphic Encryption; Private AI;

D O I：

10.1007/978-3-031-57543-3_8

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This work investigates using Homomorphic Encryption (HE) to assist the security evaluation of cryptographic devices without revealing side-channel information. For the first time, we evaluate the feasibility of execution of deep learning-based side-channel analysis on standard server equipment using an adapted HE protocol. By examining accuracy and execution time, it demonstrates the successful application of private SCA on both unprotected and protected cryptographic implementations. This contribution is a first step towards confidential side-channel analysis. Our study is limited to the honest-but-curious trust model, where we could reconstruct the secret of an unprotected AES implementation in seconds and of a masked AES implementation in under 17 min.

引用

页码：133 / 154

页数：22

共 50 条

[11] Exploring Feature Selection Scenarios for Deep Learning-based Side-channel Analysis
Perin, Guilherme
Wu, Lichao
Picek, Stjepan
IACR Transactions on Cryptographic Hardware and Embedded Systems, 2022, 2022 (04): : 828 - 861
[12] Deep Learning-Based Side-Channel Analysis Against AES Inner Rounds
Swaminathan, Sudharshan
Chmielewski, Lukasz
Perin, Guilherme
Picek, Stjepan
APPLIED CRYPTOGRAPHY AND NETWORK SECURITY WORKSHOPS, ACNS 2022, 2022, 13285 : 165 - 182
[13] A Novel Side-Channel Archive Framework Using Deep Learning-Based Leakage Compression
Jung, Sangyun
Jin, Sunghyun
Kim, Heeseok
IEEE ACCESS, 2024, 12 : 105326 - 105336
[14] Controlling the Deep Learning-Based Side-Channel Analysis: A Way to Leverage from Heuristics
Paguada, Servio
Rioja, Unai
Armendariz, Igor
APPLIED CRYPTOGRAPHY AND NETWORK SECURITY WORKSHOPS, ACNS 2020, 2020, 12418 : 106 - 125
[15] Non-profiled deep learning-based side-channel attacks with sensitivity analysis
Timon, Benjamin
IACR Transactions on Cryptographic Hardware and Embedded Systems, 2019, 2019 (02): : 107 - 131
[16] Improved Hybrid Bagging Resampling Framework for Deep Learning-Based Side-Channel Analysis
Hameed, Faisal
Ramesh, Sumesh Manjunath
Alkhzaimi, Hoda
COMPUTERS, 2024, 13 (08)
[17] Autoscaled-Wavelet Convolutional Layer for Deep Learning-Based Side-Channel Analysis
Bae, Daehyeon
Park, Dongjun
Kim, Gyusang
Choi, Minsig
Lee, Nayeon
Kim, Heeseok
Hong, Seokhie
IEEE ACCESS, 2023, 11 : 95381 - 95395
[18] Imbalanced Data Problems in Deep Learning-Based Side-Channel Attacks: Analysis and Solution
Ito, Akira
Saito, Kotaro
Ueno, Rei
Homma, Naofumi
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2021, 16 : 3790 - 3802
[19] Deep learning-based classification and anomaly detection of side-channel signals
Wang, Xiao
Zhou, Quan
Harer, Jacob
Brown, Gavin
Qiu, Shangran
Dou, Zhi
Wang, John
Hinton, Alan
Gonzalez, Carlos Aguayo
Chin, Peter
CYBER SENSING 2018, 2018, 10630
[20] Ablation Analysis for Multi-Device Deep Learning-Based Physical Side-Channel Analysis
Wu, Lichao
Won, Yoo-Seung
Jap, Dirmanto
Perin, Guilherme
Bhasin, Shivam
Picek, Stjepan
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, 2024, 21 (03) : 1331 - 1341

← 1 2 3 4 5 →