Quantifying differences or similarities in connectomes between individuals has been a challenge due to the immense complexity of global brain networks. Here we introduce a noninvasive method that uses diffusion MRI to characterize whole-brain white matter architecture as a single local connectome fingerprint that allows for a direct quantification of differences or similarities between two structural connectomes. In four independently acquired datasets with repeated scans (total N=213), we show that the local connectome fingerprint is highly specific to an individual, allowing for an accurate self-versus-others classification that achieves 100% accuracy in across 17,398 identification tests. The estimated classification error was approximately one thousand times smaller than fingerprints derived from fractional anisotropy or region-to-region connectivity patterns. We further illustrate that the local connectome fingerprint can be used as a phenotype, revealing 12.51% similarity between monozygotic twins, 5.14% between dizygotic twins, and 4.51% between none-twin siblings. Finally we show that the local connectome fingerprint can quantify neuroplasticity overtime, reflected as a decrease in self-similarity at an average rate of 7.26% per year. This novel quantification approach opens a new door for probing the influence of pathological, genetic, social, or environmental factors on the unique configuration of the human connectome.