Correlation Power Analysis with a Leakage Model

By Éric Brier, Christophe Clavier, Francis Olivier. Workshop on Cryptographic Hardware and Embedded Systems, 2004

2,722 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/e4696bc396d0e7424045c8b1e687a5588d5b8fbf

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Citations in the corpus

ChipWhisperer: An Open-Source Platform for Hardware Embedded Security Research (2014, methodology): “Instead the form shown in equation 2 is used [15].”
Pushing the Limits: A Very Compact and a Threshold Implementation of AES (2011, background): “What should directly lead to a successful attack is a CPA using HD model which predicts bit flips on a part of the state register when S-box outputs are overwritten to each other.”
A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks (extended version) (2009, background): “the Hamming weight) of the previously defined values: M(s,vs∗), as in correlation attacks [5].”
Mutual Information Analysis (2008, methodology): “This phenomenon is also observed for other distinguishers and studied in detail in [2].”
Breaking Cryptographic Implementations Using Deep Learning Techniques (2016, background): “The set of non-profiled attacks includes Differential Power Analysis (DPA) [30], Correlation Power Analysis (CPA) [9] and Mutual Information Analysis (MIA) [20].”
Mutual Information Analysis: a Comprehensive Study (2011, methodology): “For example, in the case of a correlation attack [2], one simply needs to compute: dj = ρ̂(Xj,P , Yk,P ), where the hat sign indicates that we use an estimator.”
CSI NN: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel (2019, methodology): “Next, statistical tests like correlation [6] are applied to distinguish the correct key hypothesis from other wrong guesses.”
IoT Goes Nuclear: Creating a ZigBee Chain Reaction (2017, background): “[30] uses a more complex leakage assumption, such as the one that the number of bits set to ‘1’ on a data bus has a linear relationship with the power consumption at the moment on time the data is manipulated.”
Statistical Analysis of Second Order Differential Power Analysis (2009, background): “In some cases, it may be sound to assume that the device does not leak the Hamming weight of the processed data but the Hamming distance between these data and an initial state (see, for instance, [17]).”
On the Power of Power Analysis in the Real World: A Complete Break of the KeeLoqCode Hopping Scheme (2008, methodology): “We execute a correlation DPA attack (CPA) [2] based on the following hypothetical power model”
Introduction to Side-Channel Attacks (2010, methodology): “Therefore, if we store the hypothetical Hamming weight models in a vector mqs∗ = HW(v q s∗) the empirical correlation coefficient can be used for comparison [5] :”
The World is Not Enough: Another Look on Second-Order DPA (2010, neutral): “Eventually, the combined physical leakages are compared with the key-dependent predictions, thanks to Pearson’s correlation coefficient [1].”
Template Attacks in Principal Subspaces (2006, neutral): “Finally, TAs use maximum likelihood as similarity measure, that can capture any type of dependency (if the probabilistic model is found to be adequate), whereas, for example correlation analysis only captures linear dependencies [1].”
I Know What You See: Power Side-Channel Attack on Convolutional Neural Network Accelerators (2018, background): “By analyzing the difference of multiple power traces with diverse inputs, attackers are able to uncover the secret key in widely used symmetric encryption standards, such as DES [27] and AES [28].”