Paper 2016/1152

Leak Me If You Can: Does TVLA Reveal Success Rate?

Debapriya Basu Roy, Shivam Bhasin, Sylvain Guilley, Annelie Heuser, Sikhar Patranabis, and Debdeep Mukhopadhyay

Abstract

Test Vector Leakage Assessment Methodology (TVLA) has emerged as a popular side-channel testing methodology as it can detect the presence of side-channel information in leakage measurements. However, in its current form, TVLA results cannot be used to quantify side-channel vulnerability. In this paper, we extend the TVLA testing beyond its current scope. Precisely, we derive concrete relationship between TVLA and signal to noise ratio (SNR). The linking of the two metrics, allows direct computation of success rate (SR) from TVLA, and thus unify these popular side channel detection and evaluation metrics. This, to our knowledge, is the first work in this direction. An end-to-end methodology is proposed, which can be easily automated, to derive attack SR starting from TVLA testing. The proposed methodology can take leakage model as a input and report attack SR which is validated on simulated and practical measurements. Not to surprise, the methodology performs better when the leakage model is accurately profiled. The methodology, although still limited to first-order leakage, is also further extended to (first order) multivariate setting.

Metadata
Available format(s)
PDF
Publication info
Preprint. MINOR revision.
Keywords
TVLASide ChannelEvaluation
Contact author(s)
dbroy24 @ gmail com
History
2017-07-03: last of 2 revisions
2016-12-21: received
See all versions
Short URL
https://ia.cr/2016/1152
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2016/1152,
      author = {Debapriya Basu Roy and Shivam Bhasin and Sylvain Guilley and Annelie Heuser and Sikhar Patranabis and Debdeep Mukhopadhyay},
      title = {Leak Me If You Can: Does TVLA Reveal Success Rate?},
      howpublished = {Cryptology ePrint Archive, Paper 2016/1152},
      year = {2016},
      note = {\url{https://eprint.iacr.org/2016/1152}},
      url = {https://eprint.iacr.org/2016/1152}
}
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