Paper 2017/939

Towards Optimal Pre-processing in Leakage Detection

Changhai Ou, Degang Sun, Zhu Wang, and Xinping Zhou


An attacker or evaluator can detect more information leakages if he improves the Signal-to-Noise Ratio (SNR) of power traces in his tests. For this purpose, pre-processings such as de-noise, distribution-based traces biasing are used. However, the existing traces biasing schemes can't accurately express the characteristics of power traces with high SNR, making them not ideal for leakage detections. Moreover, if the SNR of power traces is very low, it is very difficult to use the existing de-noise schemes and traces biasing schemes to enhance leakage detection. In this paper, a known key based pre-processing tool named Traces Linear Optimal Biasing (TLOB) is proposed, which performs very well even on power traces with very low SNR. It can accurately evaluate the noise of time samples and give reliable traces optimal biasing. Experimental results show that TLOB significantly reduces number of traces used for detection; correlation coefficients in $\rho$-tests using TLOB approach 1.00, thus the confidence of tests is significantly improved. As far as we know, there is no pre-processing tool more efficient than TLOB. TLOB is very simple, and only brings very limited time and memory consumption. We strongly recommend to use it to pre-process traces in side channel evaluations.

Note: This is our new submission. The paper is revised according to editor's review.

Available format(s)
Publication info
Preprint. MINOR revision.
traces optimal biasingTOBTLOBleakage detectionbiasing power tracesSNRCPAside channel attack
Contact author(s)
ouchanghai @ iie ac cn
2017-09-27: received
Short URL
Creative Commons Attribution


      author = {Changhai Ou and Degang Sun and Zhu Wang and Xinping Zhou},
      title = {Towards Optimal Pre-processing in Leakage Detection},
      howpublished = {Cryptology ePrint Archive, Paper 2017/939},
      year = {2017},
      note = {\url{}},
      url = {}
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