scholarly journals Recovering Secrets From Prefix-Dependent Leakage

2020 ◽  
Vol 14 (1) ◽  
pp. 15-24
Author(s):  
Houda Ferradi ◽  
Rémi Géraud ◽  
Sylvain Guilley ◽  
David Naccache ◽  
Mehdi Tibouchi

AbstractWe discuss how to recover a secret bitstring given partial information obtained during a computation over that string, assuming the computation is a deterministic algorithm processing the secret bits sequentially. That abstract situation models certain types of side-channel attacks against discrete logarithm and RSA-based cryptosystems, where the adversary obtains information not on the secret exponent directly, but instead on the group or ring element that varies at each step of the exponentiation algorithm.Our main result shows that for a leakage of a single bit per iteration, under suitable statistical independence assumptions, one can recover the whole secret bitstring in polynomial time. We also discuss how to cope with imperfect leakage, extend the model to k-bit leaks, and show how our algorithm yields attacks on popular cryptosystems such as (EC)DSA.

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.


2021 ◽  
Vol 21 (3) ◽  
pp. 1-20
Author(s):  
Mohamad Ali Mehrabi ◽  
Naila Mukhtar ◽  
Alireza Jolfaei

Many Internet of Things applications in smart cities use elliptic-curve cryptosystems due to their efficiency compared to other well-known public-key cryptosystems such as RSA. One of the important components of an elliptic-curve-based cryptosystem is the elliptic-curve point multiplication which has been shown to be vulnerable to various types of side-channel attacks. Recently, substantial progress has been made in applying deep learning to side-channel attacks. Conceptually, the idea is to monitor a core while it is running encryption for information leakage of a certain kind, for example, power consumption. The knowledge of the underlying encryption algorithm can be used to train a model to recognise the key used for encryption. The model is then applied to traces gathered from the crypto core in order to recover the encryption key. In this article, we propose an RNS GLV elliptic curve cryptography core which is immune to machine learning and deep learning based side-channel attacks. The experimental analysis confirms the proposed crypto core does not leak any information about the private key and therefore it is suitable for hardware implementations.


2021 ◽  
pp. 1-1
Author(s):  
Youshui Lu ◽  
Yong Qi ◽  
Saiyu Qi ◽  
Fuyou Zhang ◽  
Wei Wei ◽  
...  

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