Advertisement

On the Optimal Pre-processing for Non-profiling Differential Power Analysis

  • Suvadeep HajraEmail author
  • Debdeep Mukhopadhyay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8622)

Abstract

Differential Power Analysis (DPA) is often preceded by various noise reduction techniques. Digital Signal Processing (DSP) and Principal Component Analysis (PCA) have found their numerous applications in this area. However, most of them either require explicit profiling/semi-profiling step or depend on some heuristically chosen parameters. In this paper, we propose optimal pre-processing of power traces in non-profiling setup using an optimum linear filter and an approximate optimum linear filter. We have also empirically evaluated the proposed filters in several noisy scenarios which show significant improvements in the results of Correlation Power Analysis (CPA) over the existing pre-processing techniques. We have further investigated the optimality of the one proposed pre-processing technique by comparing it with a profiling attack.

Keywords

Discrete Fourier Transform Matched Filter Differential Power Analysis Power Trace Correlation Power Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank Shivam Bhasin of TELECOM-ParisTech, France for pointing out the window selection methods using NICV. This research work is partially funded by Department of Information Technology, India.

Supplementary material

References

  1. 1.
    Kocher, P.C., Jaffe, J., Jun, B.: Differential power analysis. In: Wiener, M. (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 388–397. Springer, Heidelberg (1999) CrossRefGoogle Scholar
  2. 2.
    Mangard, S., Oswald, E., Popp, T.: Power Analysis Attacks - Revealing the Secrets of Smart Cards. Springer, New York (2007)zbMATHGoogle Scholar
  3. 3.
    Messerges, T.S., Dabbish, E.A., Sloan, R.H.: Investigations of power analysis attacks on smartcards. In: USENIX Workshop on Smartcard Technology, pp. 151–162 (1999)Google Scholar
  4. 4.
    Clavier, C., Coron, J.-S., Dabbous, N.: Differential power analysis in the presence of hardware countermeasures. In: Koç, Ç.K., Paar, C. (eds.) CHES 2000. LNCS, vol. 1965, pp. 252–263. Springer, Heidelberg (2000) CrossRefGoogle Scholar
  5. 5.
    Rechberger, C., Oswald, E.: Practical template attacks. In: Lim, C.H., Yung, M. (eds.) WISA 2004. LNCS, vol. 3325, pp. 440–456. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  6. 6.
    Gebotys, C.H., Ho, S., Tiu, C.C.: EM analysis of Rijndael and ECC on a Wireless Java-based PDA. In: Rao, J.R., Sunar, B. (eds.) CHES 2005. LNCS, vol. 3659, pp. 250–264. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  7. 7.
    Plos, T., Hutter, M., Feldhofer, M.: On comparing side-channel preprocessing techniques for attacking RFID devices. In: Youm, H.Y., Yung, M. (eds.) WISA 2009. LNCS, vol. 5932, pp. 163–177. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  8. 8.
    Barenghi, A., Pelosi, G., Teglia, Y.: Improving first order differential power attacks through digital signal processing. In: Makarevich, O.B., Elçi, A., Orgun, M.A., Huss, S.A., Babenko, L.K., Chefranov, A.G., Varadharajan, V. (eds.) SIN, pp. 124–133. ACM, New York (2010)Google Scholar
  9. 9.
    Kasper, T., Oswald, D., Paar, C.: Side-channel analysis of cryptographic RFIDs with analog demodulation. In: Juels, A., Paar, C. (eds.) RFIDSec 2011. LNCS, vol. 7055, pp. 61–77. Springer, Heidelberg (2012) Google Scholar
  10. 10.
    Souissi, Y., Nassar, M., Guilley, S., Danger, J.-L., Flament, F.: First principal components analysis: a new side channel distinguisher. In: Rhee, K.-H., Nyang, D.H. (eds.) ICISC 2010. LNCS, vol. 6829, pp. 407–419. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  11. 11.
    Batina, L., Hogenboom, J., van Woudenberg, J.G.J.: Getting more from PCA: first results of using principal component analysis for extensive power analysis. In: Dunkelman, O. (ed.) CT-RSA 2012. LNCS, vol. 7178, pp. 383–397. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  12. 12.
    Oswald, D., Paar, C.: Improving side-channel analysis with optimal linear transforms. In: Mangard, S. (ed.) CARDIS 2012. LNCS, vol. 7771, pp. 219–233. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  13. 13.
    Hajra, S., Mukhopadhyay, D.: Pushing the limit of non-profiling DPA using multivariate leakage model. Cryptology ePrint Archive, Report 2013/849 (2013). http://eprint.iacr.org/
  14. 14.
    Standaert, F.-X., Malkin, T.G., Yung, M.: A unified framework for the analysis of side-channel key recovery attacks. In: Joux, A. (ed.) EUROCRYPT 2009. LNCS, vol. 5479, pp. 443–461. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  15. 15.
    Le, T.-H., Clédière, J., Canovas, C., Robisson, B., Servière, C., Lacoume, J.-L.: A proposition for correlation power analysis enhancement. In: Goubin, L., Matsui, M. (eds.) CHES 2006. LNCS, vol. 4249, pp. 174–186. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  16. 16.
    Brier, E., Clavier, C., Olivier, F.: Correlation power analysis with a leakage model. In: Joye, M., Quisquater, J.-J. (eds.) CHES 2004. LNCS, vol. 3156, pp. 16–29. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  17. 17.
    Doget, J., Prouff, E., Rivain, M., Standaert, F.-X.: Univariate side channel attacks and leakage modeling. J. Cryptogr. Eng. 1(2), 123–144 (2011)CrossRefGoogle Scholar
  18. 18.
    Akkar, M.-L., Bévan, R., Dischamp, P., Moyart, D.: Power analysis, what is now possible. In: Okamoto, T. (ed.) ASIACRYPT 2000. LNCS, vol. 1976, p. 489. Springer, Heidelberg (2000) CrossRefGoogle Scholar
  19. 19.
    Coron, J.-S., Naccache, D., Kocher, P.C.: Statistics and secret leakage. ACM Trans. Embed. Comput. Syst. 3(3), 492–508 (2004)CrossRefGoogle Scholar
  20. 20.
    Moradi, A., Mischke, O., Eisenbarth, T.: Correlation-enhanced power analysis collision attack. In: Mangard, S., Standaert, F.-X. (eds.) CHES 2010. LNCS, vol. 6225, pp. 125–139. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  21. 21.
    Bhasin, S., Danger, J.-L., Guilley, S., Najm, Z.: NICV: normalized inter-class variance for detection of side-channel leakage. Cryptology ePrint Archive, Report 2013/717 (2013). http://eprint.iacr.org/
  22. 22.
    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) CrossRefGoogle Scholar
  23. 23.
    Gierlichs, B., Lemke-Rust, K., Paar, C.: Templates vs. stochastic methods. In: Goubin, L., Matsui, M. (eds.) CHES 2006. LNCS, vol. 4249, pp. 15–29. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  24. 24.
    Schindler, W., Lemke, K., Paar, C.: A stochastic model for differential side channel cryptanalysis. In: Rao, J.R., Sunar, B. (eds.) CHES 2005. LNCS, vol. 3659, pp. 30–46. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  25. 25.
    Katashita, T., Satoh, A., Sugawara, T., Homma, N., Aoki, T.: Development of side-channel attack standard evaluation environment. In: European Conference on Circuit Theory and Design 2009, ECCTD 2009, pp. 403–408 (2009)Google Scholar
  26. 26.
    Tian, Q., Huss, S.A.: Power amount analysis: an efficient means to reveal the secrets in cryptosystems. Int. J. Cyber-Secur. Digit. Forensics 1(2), 99–114 (2012)Google Scholar
  27. 27.
    Sills, J., Kamen, E.: Time-varying matched filters. Circuits Syst. Sign. Process. 15(5), 609–630 (1996). http://dx.doi.org/10.1007/BF01188985 [Online]CrossRefzbMATHGoogle Scholar
  28. 28.
    Wikipedia: Matched filter – Wikipedia, The Free Encyclopedia (2013). http://en.wikipedia.org/wiki/. Accessed 20 December 2013 [Online]

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology KharagpurKharagpurIndia

Personalised recommendations