8 It Started with Templates: The Future of Profiling in Side-Channel Analysis
145
• Dynamic and Differential CMOS Logic. Tiri et al. [557] proposed Sense
Amplifier Based Logic (SABL)—a logic style that uses a fixed amount of charge
for every transition, including the degenerated events in which a gate does not
change state.
• Leakage Resilience. Another countermeasure, typically applied at the system
level, focuses on restricting the number of usages of the same key for an
algorithm. Still, generation and synchronization of new keys have practical
issues. Dziembowski et al. introduced a technique called leakage resilience,
which relocates this problem to the protocol level by introducing an algorithm
to generate these keys [195].
• Masking. One of the most efficient and powerful approaches against SCAs is
masking [134, 243], which aims to break the correlation between the power traces
and the intermediate values of the computations. This method achieves security
by randomizing the intermediate values using secret sharing and carrying out all
the computations on the shared values.
8.7 Conclusions
In this chapter, we discussed profiling side-channel attacks where we started with
data preprocessing and feature engineering. Then we presented several templatelike techniques and afterward machine learning techniques. Next, we discussed how
to conduct a sound machine learning analysis that should result in reproducible
experiments. We finished the chapter with a short discussion on how to test the
performance of SCA and what are some of the possible countermeasures to make
such attacks more difficult.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate
credit to the original author(s) and the source, provide a link to the Creative Commons licence and
indicate if changes were made.
The images or other third party material in this chapter are included in the chapter’s Creative
Commons licence, unless indicated otherwise in a credit line to the material. If material is not
included in the chapter’s Creative Commons licence and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder.
145
• Dynamic and Differential CMOS Logic. Tiri et al. [557] proposed Sense
Amplifier Based Logic (SABL)—a logic style that uses a fixed amount of charge
for every transition, including the degenerated events in which a gate does not
change state.
• Leakage Resilience. Another countermeasure, typically applied at the system
level, focuses on restricting the number of usages of the same key for an
algorithm. Still, generation and synchronization of new keys have practical
issues. Dziembowski et al. introduced a technique called leakage resilience,
which relocates this problem to the protocol level by introducing an algorithm
to generate these keys [195].
• Masking. One of the most efficient and powerful approaches against SCAs is
masking [134, 243], which aims to break the correlation between the power traces
and the intermediate values of the computations. This method achieves security
by randomizing the intermediate values using secret sharing and carrying out all
the computations on the shared values.
8.7 Conclusions
In this chapter, we discussed profiling side-channel attacks where we started with
data preprocessing and feature engineering. Then we presented several templatelike techniques and afterward machine learning techniques. Next, we discussed how
to conduct a sound machine learning analysis that should result in reproducible
experiments. We finished the chapter with a short discussion on how to test the
performance of SCA and what are some of the possible countermeasures to make
such attacks more difficult.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate
credit to the original author(s) and the source, provide a link to the Creative Commons licence and
indicate if changes were made.
The images or other third party material in this chapter are included in the chapter’s Creative
Commons licence, unless indicated otherwise in a credit line to the material. If material is not
included in the chapter’s Creative Commons licence and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder.