Effect of side channel attacks on RSA embedded devices

Author(s):  
Santosh Ghosh ◽  
Monjur Alam ◽  
Dipanwita Roy Chowdhury ◽  
Indranil Sen Gupta
Author(s):  
Zhe Liu ◽  
Patrick Longa ◽  
Geovandro Pereira ◽  
Oscar Reparaz ◽  
Hwajeong Seo

Author(s):  
Florian Bache ◽  
Clara Paglialonga ◽  
Tobias Oder ◽  
Tobias Schneider ◽  
Tim Güneysu

With the NIST post-quantum standardization competition entering the second round, the interest in practical implementation results of the remaining NIST candidates is steadily growing. Especially implementations on embedded devices are often not protected against side-channel attacks, such as differential power analysis. In this regard, the application of countermeasures against side-channel attacks to candidates of the NIST standardization process is still an understudied topic. Our work aims to contribute to the NIST competition by enabling a more realistic judgment of the overhead cost introduced by side-channel countermeasures that are applied to lattice-based KEMs that achieve CCA-security based on the Fujisaki-Okamoto transform. We present a novel higher-order masking scheme that enables an efficient comparison of polynomials as previous techniques based on arithmetic-to-Boolean conversions renders this (generally inexpensive) component extremely expensive in the masked case. Our approach has linear complexity in the number of shares compared to quadratic complexity of previous contributions and it applies to lattice based schemes with prime modulus. It comes with a proof in the probing model and an efficient implementation on an ARM Cortex-M4F microcontroller which was defined as a preferred evaluation platform for embedded implementations by NIST. Our algorithm can be executed in only 1.5-2.2 milliseconds on the target platform (depending on the masking order) and is therefore well suited even for lightweight applications. While in previous work, practical side-channel experiments were conducted using only 5,000 - 100,000 power traces, we confirm the absence of first-order leakage in this work by collecting 1 million power traces and applying the t-test methodology.


2009 ◽  
Vol 19 (11) ◽  
pp. 2990-2998 ◽  
Author(s):  
Tao ZHANG ◽  
Ming-Yu FAN

2021 ◽  
Vol 13 (6) ◽  
pp. 146
Author(s):  
Somdip Dey ◽  
Amit Kumar Singh ◽  
Klaus McDonald-Maier

Side-channel attacks remain a challenge to information flow control and security in mobile edge devices till this date. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing cores are observed over time in order to deduce security flaws. In this paper, we study how computer vision-based convolutional neural networks (CNNs) could be used to exploit temperature (thermal) side-channel attack on different Linux governors in mobile edge device utilizing multi-processor system-on-chip (MPSoC). We also designed a power- and memory-efficient CNN model that is capable of performing thermal side-channel attack on the MPSoC and can be used by industry practitioners and academics as a benchmark to design methodologies to secure against such an attack in MPSoC.


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