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

Abstract

This article reconceptualizes privacy as epistemic impedance within networked inference systems. Rather than
treating 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 V
denotes knowledge pressure and Z denotes total privacy impedance. Deep Personal Privacy (DPP) is defined as
the time-integrated inverse of effective knowledge flow. I demonstrate that privacy degrades nonlinearly under
parallel integration, proves key monotonicity properties, and show how DPP yields enforceable regulatory
thresholds. 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.