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A Novel Use of Kernel Discriminant Analysis as a Higher-Order Side-Channel Distinguisher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationSmart Card Research and Advanced Applications
Subtitle of host publication16th International Conference, CARDIS 2017, Lugano, Switzerland, November 13–15, 2017, Revised Selected Papers
Publisher or commissioning bodySpringer, Cham
Number of pages18
ISBN (Electronic)9783319752082
ISBN (Print)9783319752075
DateAccepted/In press - 12 Sep 2017
DatePublished (current) - 26 Jan 2018

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Distinguishers play an important role in Side Channel Analysis (SCA), where real world leakage information is compared against hypothetical predictions in order to guess at the underlying secret key. However, the direct relationship between leakages and predictions can be disrupted by the mathematical combining of dd random values with each sensitive intermediate value of the cryptographic algorithm (a so-called ``dd-th order masking scheme''). In the case of software implementations, as long as the masking has been correctly applied, the guessable intermediates will be independent of any one point in the trace, or indeed of any tuple of fewer than d+1d+1 points. However, certain d+1d+1-tuples of time points may jointly depend on the guessable intermediates. A typical approach to exploiting this data dependency is to pre-process the trace -- computing carefully chosen univariate functions of all possible d+1d+1-tuples -- before applying the usual univariate distinguishers. This has a computational complexity which is exponential in the order dd of the masking scheme. In this paper, we propose a new distinguisher based on Kernel Discriminant Analysis (KDA) which directly exploits properties of the mask implementation without the need to exhaustively pre-process the traces, thereby distinguishing the correct key with lower complexity.

    Research areas

  • Kernel Discriminant Analysis, Higher-order side channel analysis, Side channel distinguisher




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