This paper introduces an empirical extension of the Deep Personal Privacy (DPP) framework, a novel paradigm that reconceptualizes privacy as resistance to inference rather than mere control over data disclosure. Unlike traditional privacy-preserving approaches—such as k-anonymity, l-diversity, t-closeness, and differential privacy—which primarily focus on data access and identifiability, the DPP framework models privacy as impedance within an inference network. The core contribution of this work lies in operationalizing DPP within embedding-based systems, particularly large language models (LLMs), where sensitive information can be inferred through epistemic alignment rather than explicit disclosure. We provide a formal mathematical foundation linking cosine similarity, inference probability, and privacy impedance, and demonstrate that reducing semantic coupling systematically increases resistance to inference. Through empirical analysis on medical and social media textual data, we show that DPP-based mechanisms—such as embedding perturbation, abstraction, and dual-shifting transformations—effectively weaken inference pathways while preserving semantic utility. Furthermore, we introduce a novel regulatory interpretation of privacy through the parameter K, enabling privacy to be enforced as a measurable and auditable constraint on inference capability. This work contributes a new layer to privacy protection in the ICT era by shifting the focus from data protection to inference control, offering both a theoretical and practical framework for designing privacy-preserving AI systems.