208
A. Solanas et al.
since they collect a variety of health-related data (e.g., physiologic, biometric,
exercise, calorie intake). Furthermore, UCS-based services in smart homes and
autonomous vehicles might also put in danger query privacy since the submitted
queries could be used to extract information about daily habits, such as work
schedules or sleep routines. Finally, one of the most challenging UCS services that
could endanger query privacy are those related to voice recognition, since they listen
to and record the exact query. For this kind of service, it would be necessary to
guarantee that the signal processing is done on the device, which currently is not the
case for most services.
12.3.3 Location Privacy
One of the most significant revolutions provided by UCS is their capability to
bring computation anywhere. The deployment of UCS devices around the globe
has indirectly led to the control and monitoring of their physical location.
This situation may raise some privacy concerns since location of users of such
devices could be inferred. Location data needs to be carefully managed. It is worth
noting that with location information, other sensitive data could be inferred, e.g.,
health-related data, religious or political beliefs, or even social relationships. The
importance of preserving individuals’ location privacy in the context of UCS-based
services justifies its addition as an independent dimension to be analyzed. Location
privacy concentrates on guaranteeing the preservation of the physical location of
individuals when accessing UCS-based services.
Classical location-based services (LBS), which could be integrated into UCS
devices, require location data to provide their services (e.g., roadside assistance,
real-time traffic information or proximity-based marketing). Normally, UCS service
providers receive location information directly from individuals that use their
services. For instance, requiring the weather forecast information or the best route
to go to a specific location according to the real-time state of the traffic are services
where individuals disclose their location information explicitly. Besides, many
UCS devices, such as smartphones, smart watches, fitness trackers or autonomous
vehicles, already integrate built-in sensors with location capabilities, commonly
GPS-based.
Moreover, there are situations in which UCS providers could infer the location of
individuals by using proximity data. For instance, video surveillance systems could
identify individuals (e.g., by using face recognition) and associate their location
with that of the camera, without the intervention of the user. Also, in the case of
autonomous cars and smart homes, the location of users is indirectly disclosed since
it coincides with the location of the car and the home, respectively.
The sensitiveness of location data fosters the search for solutions that allow
the hiding of location information while preserving functionality. For example,
in scenarios where the location of the UCS changes over time, collaboration
mechanisms between nearby UCS devices/users could mask exact locations, so that
A. Solanas et al.
since they collect a variety of health-related data (e.g., physiologic, biometric,
exercise, calorie intake). Furthermore, UCS-based services in smart homes and
autonomous vehicles might also put in danger query privacy since the submitted
queries could be used to extract information about daily habits, such as work
schedules or sleep routines. Finally, one of the most challenging UCS services that
could endanger query privacy are those related to voice recognition, since they listen
to and record the exact query. For this kind of service, it would be necessary to
guarantee that the signal processing is done on the device, which currently is not the
case for most services.
12.3.3 Location Privacy
One of the most significant revolutions provided by UCS is their capability to
bring computation anywhere. The deployment of UCS devices around the globe
has indirectly led to the control and monitoring of their physical location.
This situation may raise some privacy concerns since location of users of such
devices could be inferred. Location data needs to be carefully managed. It is worth
noting that with location information, other sensitive data could be inferred, e.g.,
health-related data, religious or political beliefs, or even social relationships. The
importance of preserving individuals’ location privacy in the context of UCS-based
services justifies its addition as an independent dimension to be analyzed. Location
privacy concentrates on guaranteeing the preservation of the physical location of
individuals when accessing UCS-based services.
Classical location-based services (LBS), which could be integrated into UCS
devices, require location data to provide their services (e.g., roadside assistance,
real-time traffic information or proximity-based marketing). Normally, UCS service
providers receive location information directly from individuals that use their
services. For instance, requiring the weather forecast information or the best route
to go to a specific location according to the real-time state of the traffic are services
where individuals disclose their location information explicitly. Besides, many
UCS devices, such as smartphones, smart watches, fitness trackers or autonomous
vehicles, already integrate built-in sensors with location capabilities, commonly
GPS-based.
Moreover, there are situations in which UCS providers could infer the location of
individuals by using proximity data. For instance, video surveillance systems could
identify individuals (e.g., by using face recognition) and associate their location
with that of the camera, without the intervention of the user. Also, in the case of
autonomous cars and smart homes, the location of users is indirectly disclosed since
it coincides with the location of the car and the home, respectively.
The sensitiveness of location data fosters the search for solutions that allow
the hiding of location information while preserving functionality. For example,
in scenarios where the location of the UCS changes over time, collaboration
mechanisms between nearby UCS devices/users could mask exact locations, so that