The pervasive integration of Indoor Positioning Systems (IPS) arises from the limitations of Global Navigation Satellite Systems (GNSS) in indoor environments, leading to the widespread adoption of Location-Based Services (LBS) in places such as shopping malls, airports, hospitals, museums, corporate campuses, and smart buildings. Specifically, indoor location fingerprinting (ILF) systems employ diverse signal fingerprints from user devices, enabling precise location identification by Location Service Providers (LSP). Despite its broad applications across various domains, ILF introduces a notable privacy risk, as both LSP and potential adversaries inherently have access to this sensitive information, compromising users' privacy. Consequently, concerns regarding privacy vulnerabilities in this context necessitate a focused exploration of privacy-preserving mechanisms. In response to these concerns, this survey presents a comprehensive review of Indoor Location Fingerprinting Privacy-Preserving Mechanisms (ILFPPM) based on cryptographic, anonymization, differential privacy (DP), and federated learning (FL) techniques. We also propose a distinctive and novel grouping of privacy vulnerabilities, adversary models, privacy attacks, and evaluation metrics specific to ILF systems. Given the identified limitations and research gaps in this survey, we highlight numerous prospective opportunities for future investigation, aiming to motivate researchers interested in advancing ILF systems. This survey constitutes a valuable reference for researchers and provides a clear overview for those beyond this specific research domain. To further help the researchers, we have created an online resource repository, which can be found at \href{https://github.com/amir-ftlz/ilfppm}{https://github.com/amir-ftlz/ilfppm}.

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