Skip to main content
SpringerLink
Log in

Towards Sound and Optimal Leakage Detection Procedure

  • Conference paper
  • First Online:
Smart Card Research and Advanced Applications (CARDIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10728))

Abstract

Evaluation of side-channel leakage for cryptographic systems requires sound leakage detection procedures. The commonly used standard approach is the test vector leakage assessment (TVLA) procedure. We first relate TVLA to the statistical minimum p-value (mini-p) procedure, and propose a sound method of deciding leakage existence in the statistical hypothesis setting. An advanced statistical procedure, Higher Criticism (HC), is adopted to improve leakage detection when there are multiple leakage points. The HC-based procedure is optimal in side-channel leakage detection, because for a given number of traces with a given length, it detects the existence of leakage at the signal level as low as possibly detectable by any statistical procedure. Numerical studies show that our HC-based procedure perform as well as the mini-p based procedure when leakage signals are very sparse, and can improve the leakage detection significantly when there are multiple leakages.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Goodwill, G., Jun, B., Jaffe, J., Rohatgi, P.: A testing methodology for side-channel resistance validation. In: NIST Non-Invasive Attack Testing Workshop, September 2011. http://csrc.nist.gov/news_events/non-invasive-attack-testing-workshop/papers/08_Goodwill.pdf

  2. Cooper, J., DeMulder, E., Goodwill, G., Jaffe, J., Kenworthy, G., Rohatgi, P.: Test vector leakage assessment (TVLA) methodology in practice. In: International Cryptographic Module Conference (2013). http://icmc-2013.org/wp/wp-content/uploads/2013/09/goodwillkenworthtestvector.pdf

  3. Mather, L., Oswald, E., Bandenburg, J., Wójcik, M.: Does my device leak information? an a priori statistical power analysis of leakage detection tests. In: Sako, K., Sarkar, P. (eds.) ASIACRYPT 2013. LNCS, vol. 8269, pp. 486–505. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-42033-7_25

    Chapter  Google Scholar 

  4. Schneider, T., Moradi, A.: Leakage assessment methodology. In: Güneysu, T., Handschuh, H. (eds.) CHES 2015. LNCS, vol. 9293, pp. 495–513. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48324-4_25

    Chapter  Google Scholar 

  5. Durvaux, F., Standaert, F.-X.: From improved leakage detection to the detection of points of interests in leakage traces. In: Fischlin, M., Coron, J.-S. (eds.) EUROCRYPT 2016. LNCS, vol. 9665, pp. 240–262. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49890-3_10

    Chapter  Google Scholar 

  6. Ding, A.A., Chen, C., Eisenbarth, T.: Simpler, faster, and more robust T-test based leakage detection. In: Standaert, F.-X., Oswald, E. (eds.) COSADE 2016. LNCS, vol. 9689, pp. 163–183. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43283-0_10

    Chapter  Google Scholar 

  7. Bilgin, B., Gierlichs, B., Nikova, S., Nikov, V., Rijmen, V.: Higher-order threshold implementations. In: Sarkar, P., Iwata, T. (eds.) ASIACRYPT 2014. LNCS, vol. 8874, pp. 326–343. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45608-8_18

    Google Scholar 

  8. Nascimento, E., López, J., Dahab, R.: Efficient and secure elliptic curve cryptography for 8-bit AVR microcontrollers. In: Chakraborty, R.S., Schwabe, P., Solworth, J. (eds.) SPACE 2015. LNCS, vol. 9354, pp. 289–309. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24126-5_17

    Chapter  Google Scholar 

  9. De Cnudde, T., Bilgin, B., Reparaz, O., Nikova, S.: Higher-order glitch resistant implementation of the PRESENT S-box. In: Ors, B., Preneel, B. (eds.) BalkanCryptSec 2014. LNCS, vol. 9024, pp. 75–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21356-9_6

    Chapter  Google Scholar 

  10. Balasch, J., Gierlichs, B., Grosso, V., Reparaz, O., Standaert, F.-X.: On the cost of lazy engineering for masked software implementations. In: Joye, M., Moradi, A. (eds.) CARDIS 2014. LNCS, vol. 8968, pp. 64–81. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16763-3_5

    Google Scholar 

  11. Donoho, D., Jin, J.: Higher criticism for detecting sparse heterogeneous mixtures. Ann. Stat. 32, 962–994 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  12. Donoho, D., Jin, J.: Higher criticism thresholding: optimal feature selection when useful features are rare and weak. Proc. Nat. Acad. Sci. 105, 14790–14795 (2008)

    Article  MATH  Google Scholar 

  13. Fan, J., Lv, J.: Sure independence screening for ultra-high dimensional feature space. J. Royal Stat. Soc. Ser. B 70, 1–35 (2008)

    Article  Google Scholar 

  14. Fan, J., Feng, Y., Song, R.: Nonparametric independence screening in sparse ultra-high-dimensional additive models. J. Am. Stat. Assoc. 106(494), 544–557 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  15. Li, J., Siegmund, D., et al.: Higher criticism: \( p \)-values and criticism. Ann. Stat. 43(3), 1323–1350 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  16. Donoho, D., Jin, J., et al.: Higher criticism for large-scale inference, especially for rare and weak effects. Stat. Sci. 30(1), 1–25 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  17. Wu, Z., Sun, Y., He, S., Cho, J., Zhao, H., Jin, J.: Detection boundary and higher criticism approach for rare and weak genetic effects. Ann. Appl. Stat. 8(2), 824–851 (2014). https://doi.org/10.1214/14-AOAS724

    Article  MathSciNet  MATH  Google Scholar 

  18. Ingster, Y.I.: Minimax detection of a signal for i (n)-balls. Math. Methods Stat. 7(4), 401–428 (1998)

    MathSciNet  MATH  Google Scholar 

  19. Archambeau, C., Peeters, E., Standaert, F.-X., Quisquater, J.-J.: Template attacks in principal subspaces. In: Goubin, L., Matsui, M. (eds.) CHES 2006. LNCS, vol. 4249, pp. 1–14. Springer, Heidelberg (2006). https://doi.org/10.1007/11894063_1

    Chapter  Google Scholar 

  20. Standaert, F.-X., Archambeau, C.: Using subspace-based template attacks to compare and combine power and electromagnetic information leakages. In: Oswald, E., Rohatgi, P. (eds.) CHES 2008. LNCS, vol. 5154, pp. 411–425. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85053-3_26

    Chapter  Google Scholar 

  21. Bär, M., Drexler, H., Pulkus, J.: Improved template attacks. In: International Workshop on Constructive Side-Channel Analysis and Secure Design (2010)

    Google Scholar 

  22. Elaabid, M.A., Meynard, O., Guilley, S., Danger, J.-L.: Combined side-channel attacks. In: Chung, Y., Yung, M. (eds.) WISA 2010. LNCS, vol. 6513, pp. 175–190. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-17955-6_13

    Chapter  Google Scholar 

  23. Choudary, O., Kuhn, M.G.: Efficient template attacks. In: Francillon, A., Rohatgi, P. (eds.) CARDIS 2013. LNCS, vol. 8419, pp. 253–270. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08302-5_17

    Google Scholar 

  24. Bruneau, N., Guilley, S., Heuser, A., Marion, D., Rioul, O.: Less is more. In: Güneysu, T., Handschuh, H. (eds.) CHES 2015. LNCS, vol. 9293, pp. 22–41. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48324-4_2

    Chapter  Google Scholar 

  25. Zhang, L., Ding, A.A., Durvaux, F., Standaert, F.-X., Fei, Y.: Towards sound and optimal leakage detection procedure, Cryptology ePrint Archive, Report 2017/287 (2017). http://eprint.iacr.org/2017/287

  26. Lehmann, E.L., Romano, J.P.: Testing Statistical Hypotheses. Springer, New York (2006). https://doi.org/10.1007/0-387-27605-X

    MATH  Google Scholar 

  27. Hall, P., Jin, J.: Properties of higher criticism under strong dependence. Ann. Stat. 36, 381–402 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  28. Barnett, I., Mukherjee, R., Lin, X.: The generalized higher criticism for testing SNP-set effects in genetic association studies. J. Am. Stat. Assoc. 112(517), 64–76 (2017)

    Article  MathSciNet  Google Scholar 

  29. Mangard, S., Oswald, E., Standaert, F.X.: One for all - all for one: unifying standard differential power analysis attacks. IET Inf. Secur. 5(2), 100–110 (2011)

    Article  Google Scholar 

  30. Testbed for side channel analysis and security evaluation (2014). http://tescase.coe.neu.edu

  31. Akkar, M.-L., Giraud, C.: An implementation of DES and AES, secure against some attacks. In: Koç, Ç.K., Naccache, D., Paar, C. (eds.) CHES 2001. LNCS, vol. 2162, pp. 309–318. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44709-1_26

    Chapter  Google Scholar 

  32. Chari, S., Jutla, C.S., Rao, J.R., Rohatgi, P.: Towards sound approaches to counteract power-analysis attacks. In: Wiener, M. (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 398–412. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48405-1_26

    Chapter  Google Scholar 

  33. Schramm, K., Paar, C.: Higher order masking of the AES. In: Pointcheval, D. (ed.) CT-RSA 2006. LNCS, vol. 3860, pp. 208–225. Springer, Heidelberg (2006). https://doi.org/10.1007/11605805_14

    Chapter  Google Scholar 

  34. Prouff, E., Rivain, M., Bevan, R.: Statistical analysis of second order differential power analysis. IEEE Trans. Comput. 58(6), 799–811 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  35. Ding, A.A., Zhang, L., Fei, Y., Luo, P.: A statistical model for higher order DPA on masked devices. In: Batina, L., Robshaw, M. (eds.) CHES 2014. LNCS, vol. 8731, pp. 147–169. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44709-3_9

    Google Scholar 

Download references

Acknowledgment

This work has been funded in parts by National Science Foundation grants CNS-1314655, CNS-1337854 and CNS-1563697, and by the European Commission through the H2020 project 731591 (acronym REASSURE) and the ERC project 724725 (acronym SWORD). François-Xavier Standaert is a senior research associate of the Belgian Fund for Scientific Research (FNRS-F.R.S.).

Author information

Authors and Affiliations

  1. Department of Mathematics, Northeastern University, Boston, MA, USA

    A. Adam Ding & Liwei Zhang

  2. ICTEAM/ELEN/Crypto Group, Universite catholique de Louvain, Louvain-la-Neuve, Belgium

    Francois Durvaux & Francois-Xavier Standaert

  3. Department of ECE, Northeastern University, Boston, MA, USA

    Yunsi Fei

Authors
  1. A. Adam Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liwei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francois Durvaux
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Francois-Xavier Standaert
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yunsi Fei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Adam Ding .

Editor information

Editors and Affiliations

  1. University of Lübeck and WPI, Lübeck, Germany

    Thomas Eisenbarth

  2. Gemalto, La Ciotat, France

    Yannick Teglia

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ding, A.A., Zhang, L., Durvaux, F., Standaert, FX., Fei, Y. (2018). Towards Sound and Optimal Leakage Detection Procedure. In: Eisenbarth, T., Teglia, Y. (eds) Smart Card Research and Advanced Applications. CARDIS 2017. Lecture Notes in Computer Science(), vol 10728. Springer, Cham. https://doi.org/10.1007/978-3-319-75208-2_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-75208-2_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75207-5

  • Online ISBN: 978-3-319-75208-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation