Poly-Logarithmic Side Channel Rank Estimation via Exponential Sampling

Liron David, Avishai Wool

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationTopics in Cryptology – CT-RSA 2019 - The Cryptographers’ Track at the RSA Conference 2019, Proceedings
EditorsMitsuru Matsui
Pages330-349
Number of pages20
DOIs
StatePublished - 2019
EventCryptographers Track at the RSA Conference 2019, CT-RSA 2019 - San Francisco, United States
Duration: 4 Mar 20198 Mar 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11405 LNCS

Conference

ConferenceCryptographers Track at the RSA Conference 2019, CT-RSA 2019
Country/TerritoryUnited States
CitySan Francisco
Period4/03/198/03/19

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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