Privacy as Epistemic Impedance: Deep Personal Privacy and the Political Economy of Knowledge in Networked Societies

This article reconceptualizes privacy as epistemic impedance within networked inference systems. Rather thantreating privacy as control over data, I argue that privacy concerns the regulation of knowledge formation.Building on a structural analogy to electrical impedance, I formalize privacy current as I = V / Z, where Vdenotes knowledge pressure and Z denotes total privacy impedance. Deep Personal Privacy (DPP) is defined asthe time-integrated inverse of effective knowledge flow. I demonstrate that privacy degrades nonlinearly underparallel integration, proves key monotonicity properties, and show how DPP yields enforceable regulatorythresholds. The framework integrates information theory, graph theory, and game-theoretical modeling(Shannon, 1948; Cover, & Thomas, 2006; Doyle, & Snell, 1984; Kong, Chen, Yang, Cheng, Zhang, & He, 2023)while remaining grounded in normative commitments to epistemic symmetry and autonomy.