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Poly-Logarithmic Side Channel Rank Estimation via Exponential Sampling

  • Liron DavidEmail author
  • Avishai WoolEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11405)

Abstract

Rank estimation is an important tool for a side-channel evaluations laboratories. It allows estimating the remaining security after an attack has been performed, quantified as the time complexity and the memory consumption required to brute force the key given the leakages as probability distributions over d subkeys (usually key bytes). These estimations are particularly useful where the key is not reachable with exhaustive search.

We propose ESrank, the first rank estimation algorithm that enjoys provable poly-logarithmic time- and space-complexity, which also achieves excellent practical performance. Our main idea is to use exponential sampling to drastically reduce the algorithm’s complexity. Importantly, ESrank is simple to build from scratch, and requires no algorithmic tools beyond a sorting function. After rigorously bounding the accuracy, time and space complexities, we evaluated the performance of ESrank on a real SCA data corpus, and compared it to the currently-best histogram-based algorithm. We show that ESrank gives excellent rank estimation (with roughly a 1-bit margin between lower and upper bounds), with a performance that is on-par with the Histogram algorithm: a run-time of under 1 s on a standard laptop using 6.5 MB RAM.

Notes

Acknowledgement

Liron David was partially supported by The Yitzhak and Chaya Weinstein Research Institute for Signal Processing.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Electrical EngineeringTel Aviv UniversityTel AvivIsrael

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