Leveling the risks of timing attacks based on permutation decoding

2020 ◽  
Author(s):  
Anatolii A. Gladkikh ◽  
Dmitrii V. Mishin ◽  
Nikolai Y. Chilikhin
Keyword(s):  
Permutation Decoding ◽  
Timing Attacks

Research on Access Driven Cache Timing Attacks Against Camellia

2010 ◽  
Vol 33 (7) ◽  
pp. 1153-1164
Author(s):  
Xin-Jie ZHAO ◽  
Tao WANG ◽  
Yuan-Yuan ZHENG
Keyword(s):  
Timing Attacks

scholarly journals Timing attacks and local timing attacks against Barrett’s modular multiplication algorithm

2021 ◽  
Author(s):  
Johannes Mittmann ◽  
Werner Schindler
Keyword(s):  
Side Channel ◽  
Modular Exponentiation ◽  
Modular Multiplication ◽  
Multiplication Algorithm ◽  
Diffie Hellman ◽  
Mathematical Difficulties ◽  
Execution Times ◽  
Stochastic Properties ◽  
Timing Attacks ◽  
Theoretical Results

AbstractMontgomery’s and Barrett’s modular multiplication algorithms are widely used in modular exponentiation algorithms, e.g. to compute RSA or ECC operations. While Montgomery’s multiplication algorithm has been studied extensively in the literature and many side-channel attacks have been detected, to our best knowledge no thorough analysis exists for Barrett’s multiplication algorithm. This article closes this gap. For both Montgomery’s and Barrett’s multiplication algorithm, differences of the execution times are caused by conditional integer subtractions, so-called extra reductions. Barrett’s multiplication algorithm allows even two extra reductions, and this feature increases the mathematical difficulties significantly. We formulate and analyse a two-dimensional Markov process, from which we deduce relevant stochastic properties of Barrett’s multiplication algorithm within modular exponentiation algorithms. This allows to transfer the timing attacks and local timing attacks (where a second side-channel attack exhibits the execution times of the particular modular squarings and multiplications) on Montgomery’s multiplication algorithm to attacks on Barrett’s algorithm. However, there are also differences. Barrett’s multiplication algorithm requires additional attack substeps, and the attack efficiency is much more sensitive to variations of the parameters. We treat timing attacks on RSA with CRT, on RSA without CRT, and on Diffie–Hellman, as well as local timing attacks against these algorithms in the presence of basis blinding. Experiments confirm our theoretical results.

Codes from multipartite graphs and minimal permutation decoding sets

2015 ◽  
Vol 07 (04) ◽  
pp. 1550060
Author(s):  
P. Seneviratne
Keyword(s):  
Automorphism Group ◽  
Error Correcting Code ◽  
Binary Codes ◽  
Hard Problem ◽  
Complete Multipartite Graph ◽  
Decoding Algorithm ◽  
Permutation Decoding ◽  
Multipartite Graphs ◽  
Multipartite Graph ◽  
Complete Multipartite

Permutation decoding method developed by MacWilliams and described in [Permutation decoding of systematic codes, Bell Syst. Tech. J. 43 (1964) 485–505] is a decoding technique that uses a subset of the automorphism group of the code called a PD-set. The complexity of the permutation decoding algorithm depends on the size of the PD-set and finding a minimal PD-set for an error correcting code is a hard problem. In this paper we examine binary codes from the complete-multipartite graph [Formula: see text] and find PD-sets for all values of [Formula: see text] and [Formula: see text]. Further we show that these PD-sets are minimal when [Formula: see text] is odd and [Formula: see text].

scholarly journals End-to-end automated cache-timing attack driven by Machine Learning

10.29007/nwj8 ◽  
2019 ◽  
Author(s):  
Sebastien Carré ◽  
Victor Dyseryn ◽  
Adrien Facon ◽  
Sylvain Guilley ◽  
Thomas Perianin
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Relevant Information ◽  
Security Threats ◽  
Cache Memories ◽  
Function Calls ◽  
Timing Data ◽  
Timing Attack ◽  
End To End ◽  
Timing Attacks

Cache timing attacks are serious security threats that exploit cache memories to steal secret information.We believe that the identification of a sequence of operations from a set of cache-timing data measurements is not a trivial step when building an attack. We present a recurrent neural network model able to automatically retrieve a sequence of function calls from cache-timings. Inspired from natural language processing, our model is able to learn on partially labelled data. We use the model to unfold an end-to-end automated attack on OpenSSL ECDSA on the secp256k1 curve. Contrary to most research, we did not need human processing of the traces to retrieve relevant information.

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