Abstract
Private comparison schemes constructed on homomorphic encryption offer the noninteractive and parallelizable features, and have advantages in communication bandwidth and performance. In this work, we propose cuXCMP, an extension of the privacy comparison scheme XCMP (AsiaCCS 2018). We address the relatively small input domain and the incompletely expressible output of XCMP, by modifying the encoding method and devising a constant term extraction (CTX) approach. Then, we describe a method for constructing privacy-preserving decision tree (PPDT) using this scheme. Considering the high computational overhead of CTX, we exploit the massive parallelism of the GPU to accelerate this function. Based on the results of the kernel profiling, we utilize several optimization techniques to improve the performance, including using multiple CUDA streams, reducing the grid dimension, kernel fusion, etc. By accelerating this function, we boost the execution time of the scheme and demonstrate 130× and 1.9× speedups for CTX and cuXCMP, respectively, as well as a 35% reduction in the evaluation time of PPDT.
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