Fast Primitives for Internal Data Scrambling in Tamper Resistant Hardware

  • Eric Brier
  • Helena Handschuh
  • Christophe Tymen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2162)


Although tamper-resistant devices are specifically designed to thwart invasive attacks, they remain vulnerable to micro-probing. Among several possibilities to provide data obfuscations, keyed hardware permutations can provide compact design and easy diversification. We discuss the efficiency of such primitives, and we give several examples of implementations, along with proofs of effectively large key-space.


Tamper-resistance Probing attacks Data scrambling Keyed permutations Smart-cards 


  1. 1.
    Ross Anderson and Markus Kuhn. Tamper resistance-a Cautionary Note. In The second USENIX Workshop on Electronic Commerce Proceeding, pages 1–11, Oakland, California, November 1996.Google Scholar
  2. 2.
    Tamás Horvàth. Arithmetic Design for Permutation Groups. In Ç.K. Koç and C. Paar, editors, Cryptographic Hardware and Embedded Systems (CHES’ 99), number 1717 in Lecture Notes in Computer Science, pages 109–121. Springer Verlag, 1999.CrossRefGoogle Scholar
  3. 3.
    Olivier Kömmerling and Markus Kuhn. Design principles for Tamper-Resistant Smartcard Processors. In USENIX Workshop on Smartcard Technology, Chicago, Illinois, USA, May 1999.Google Scholar
  4. 4.
    S. Rankl and W. Effing. Smart Card Handbook. John Wiley & Sons, 1999.Google Scholar
  5. 5.
    Derek Robinson. A Course in the Theory of Groups. Number 80 in GTM. Springer Verlag, 1991.Google Scholar
  6. 6.
    Adi Shamir. Assassinating SASAS. Rump session of Crypto’ 2000.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Eric Brier
    • 1
  • Helena Handschuh
    • 2
  • Christophe Tymen
    • 3
  1. 1.Card Security GroupGemplus Card InternationalGémenosFrance
  2. 2.Gemplus Card InternationalIssy-les-MoulineauxFrance
  3. 3.École Normale SupérieureParisFrance

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