  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). Specifically, indoor location fingerprinting employs diverse signal
fingerprints from user devices, enabling precise location identification by
Location Service Providers (LSP). Despite its broad applications across various
domains, indoor location fingerprinting 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 Privacy-Preserving Mechanisms in Indoor
Location Fingerprinting (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
and attack models, and available evaluation metrics specific to indoor location
fingerprinting 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 this
field. This survey serves as a valuable reference for researchers and provides
a clear overview for those beyond this specific research domain.
