Head-worn augmented reality (AR) continues to evolve through critical advancements in power optimizations, AI capabilities, and naturalistic user interactions. Eye-tracking sensors play a key role in these advancements. At the same time, eye-tracking data is not well understood by users and can reveal sensitive information. Our work contributes visualizations based on visceral notice to increase privacy awareness of eye-tracking data in AR. We also evaluated user perceptions towards privacy noise mechanisms applied to gaze data visualized through these visceral interfaces. While privacy mechanisms have been evaluated against privacy attacks, we are the first to evaluate them subjectively and understand their influence on data-sharing attitudes. Despite our participants being highly concerned with eye-tracking privacy risks, we found 47% of our participants still felt comfortable sharing raw data. When applying privacy noise, 70% to 76% felt comfortable sharing their gaze data for the Weighted Smoothing and Gaussian Noise privacy mechanisms, respectively. This implies that participants are still willing to share raw gaze data even though overall data-sharing sentiments decreased after experiencing the visceral interfaces and privacy mechanisms. Our work implies that increased access and understanding of privacy mechanisms are critical for gaze-based AR applications; further research is needed to develop visualizations and experiences that relay additional information about how raw gaze data can be used for sensitive inferences, such as age, gender, and ethnicity. We intend to open-source our codebase to provide AR developers and platforms with the ability to better inform users about privacy concerns and provide access to privacy mechanisms. A pre-print of this paper and all supplemental materials are available at https://bmdj-vt.github.io/project_pages/privacy_notice.