Two things are meant under this, (Pearson's) correlation coefficient computation for CPA and cross-correlation computation for alignment and other uses. These are two quite different algorithms, grouped here because both are interesting and use the word "correlation".
For alignment you take a reference trace and cross-correlate every trace from the trace set with it, then find the peak
value of the result and use that as a shift to align the trace.
Two things are meant under this, (Pearson's) correlation coefficient computation for CPA and cross-correlation computation for alignment and other uses. These are two quite different algorithms, grouped here because both are interesting and use the word "correlation".
TODO
Pearson's correlation coefficient
For CPA: https://www.iacr.org/archive/ches2004/31560016/31560016.pdf where it is used to compute (samplewise) correlation coefficient between hypothesized leakage values and power leakage.
Implemented on GPU here: https://arxiv.org/abs/1412.7682 also here: https://link.springer.com/chapter/10.1007/978-3-642-27257-8_16 also here: https://dl.acm.org/doi/10.1145/2611765.2611775 also here: https://www.semanticscholar.org/paper/Improving-DPA-analysis-with-distributed-computing-Amaral-Inesc-Id/43c150d1dd42a2327e302202784d2b685347e7a4 CPU work here: https://crypto.fit.cvut.cz/sites/default/files/publications/fulltexts/pearson.pdf Some interesting work: https://informatik.rub.de/wp-content/uploads/2021/11/MA_Klostermann.pdf
More resources:
Cross-correlation
For alignment you take a reference trace and cross-correlate every trace from the trace set with it, then find the peak value of the result and use that as a shift to align the trace.