  The advent of numerous indoor location-based services (LBSs) and the
widespread use of many types of mobile devices in indoor environments have
resulted in generating a massive amount of people's location data. While
geo-spatial data contains sensitive information about personal activities,
collecting it in its raw form may lead to the leak of personal information
relating to the people, violating their privacy. This paper proposes a novel
privacy-aware framework for aggregating the indoor location data employing the
Local Differential Privacy (LDP) technique, in which the user location data is
changed locally in the user's device and is sent to the aggregator afterward.
Therefore, the users' locations are kept hidden from a server or any attackers.
The practical feasibility of applying the proposed framework is verified by two
real-world datasets. The impact of dataset properties, the privacy mechanisms,
and the privacy level on our framework are also investigated. The experimental
results indicate that the presented framework can protect the location
information of users, and the accuracy of the population frequency of different
zones in the indoor area is close to that of the original population frequency
with no knowledge about the location of people indoors.
