This paper addresses the growing privacy concerns associated with Wi-Fi sensing, a technology that uses existing wireless infrastructure to extract sensitive information such as human presence, motion, breathing, and typing. Although Wi-Fi sensing enables cost-effective and scalable applications in smart homes, healthcare, and security, its passive nature allows adversaries to eavesdrop on Wi-Fi transmissions and analyze signal patterns for private data extraction. While researchers have proposed various defense mechanisms to counter these threats, a unified framework for categorizing and comparing these approaches, particularly in terms of their trade-offs and practicality, has been notably absent. This paper aims to fill this gap by providing a structured categorization of existing defense mechanisms against Wi-Fi sensing, including a detailed analysis of their limitations, and informing the design of more robust and privacypreserving solutions. The key contributions include a comprehensive taxonomy of defense mechanisms based on their core strategy and operational layer, a comparative analysis of their effectiveness, feasibility, and trade-offs, and an identification of open challenges for future research.