Abstract:The Blum-Kalai-Wasserman algorithm (BKW) is an algorithm for solving the learning parity with noise problem, which was then adapted for solving the learning with errors problem (LWE) by Albrecht et al. Duc et al. applied BKW also to the learning with rounding problem (LWR). The number of blocks is a parameter of BKW. By optimizing the number of blocks, we can minimize the time complexity of BKW. However, Duc et al. did not derive the optimal number of blocks theoretically, but they searched for it numerically.… Show more
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