The internet provides an ideal anonymous channel for concealing computer-mediated malicious activities, as the network-based origins of critical electronic textual evidence (e.g., emails, blogs, forum posts, chat logs, etc.) can be easily repudiated. Given the anonymous documents, authorship analysis is the study of identifying the actual author and his/her socio-linguistic characteristics. Many linguistic stylometric features and computational techniques have been extensively studied for this purpose. However, most of them emphasize promoting the authorship attribution accuracy, and few works have been done for the purpose of constructing and visualizing the evidential traits. I opt for an interpretable and explainable approach by which writing styles can be visualized, compared, and interpreted by an investigator like fingerprints. I also propose to integrate differential privacy and reinforcement learning to paraphrase text where writing style is sanitized.