Using Subspace-Based Template Attacks to Compare and Combine Power and Electromagnetic Information Leakages

  • François-Xavier Standaert
  • Cedric Archambeau
Conference paper

DOI: 10.1007/978-3-540-85053-3_26

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5154)
Cite this paper as:
Standaert FX., Archambeau C. (2008) Using Subspace-Based Template Attacks to Compare and Combine Power and Electromagnetic Information Leakages. In: Oswald E., Rohatgi P. (eds) Cryptographic Hardware and Embedded Systems – CHES 2008. CHES 2008. Lecture Notes in Computer Science, vol 5154. Springer, Berlin, Heidelberg

Abstract

The power consumption and electromagnetic radiation are among the most extensively used side-channels for analyzing physically observable cryptographic devices. This paper tackles three important questions in this respect. First, we compare the effectiveness of these two side-channels. We investigate the common belief that electromagnetic leakages lead to more powerful attacks than their power consumption counterpart. Second we study the best combination of the power and electromagnetic leakages. A quantified analysis based on sound information theoretic and security metrics is provided for these purposes. Third, we evaluate the effectiveness of two data dimensionality reduction techniques for constructing subspace-based template attacks. Selecting automatically the meaningful time samples in side-channel leakage traces is an important problem in the application of template attacks and it usually relies on heuristics. We show how classical statistical tools such as Principal Component Analysis and Fisher Linear Discriminant Analysis can be used for efficiently preprocessing the leakage traces.

Download to read the full conference paper text

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • François-Xavier Standaert
    • 1
  • Cedric Archambeau
    • 2
  1. 1.UCL Crypto GroupUniversité catholique de Louvain 
  2. 2.Centre for Computational Statistics and Machine LearningUniversity College London 

Personalised recommendations