FastCPA: Efficient Correlation Power Analysis Computation with a Large Number of Traces - Archive ouverte HAL
Communication Dans Un Congrès Année : 2019

FastCPA: Efficient Correlation Power Analysis Computation with a Large Number of Traces

Résumé

Cryptographic algorithm implementations need to be secured against side-channel attacks. Correlation Power Analysis (CPA) is an efficient technique for recovering secret key bytes of a cryptographic algorithm implementation by analyzing the power traces of its execution. Although CPA usually does not require a lot of traces to recover secret key bytes, it is no longer true in a noisy environment , for which the required number of traces can be very high. Computation time can then become a major concern for performing this attack and assessing the robustness of an implementation against it. This article introduces FastCPA, which is a correlation computation targeting the same goal as regular CPA, but based on power consumption vectors indexed by plaintext values. The main advantage of FastCPA is its fast execution time compared to the regular CPA computation, especially when the number of traces is high: for 100,000 traces, the speedup factor varies from 70 to almost 200 depending on the number of samples. An analysis of FastCPA accuracy is made, based on the number of correct key bytes found with an increasing noise. This analysis shows that FastCPA performs similarly as the regular CPA for a high number of traces. The minimum required number of traces to get the correct key guess is also computed for 100,000 noisy traces and shows that FastCPA obtains similar results to those of regular CPA. Finally, although FastCPA is more sensitive to plaintext values than the regular CPA, it is shown that this aspect can be neglected for a high number of traces.
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Dates et versions

hal-02172200 , version 1 (03-07-2019)

Identifiants

Citer

Quentin L. Meunier. FastCPA: Efficient Correlation Power Analysis Computation with a Large Number of Traces. 6th Cryptography and Security in Computing Systems (CS2’19), Jan 2019, Valence, Spain. ⟨10.1145/3304080.3304082⟩. ⟨hal-02172200⟩
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