33,143 research outputs found
Location-related privacy in geo-social networks
Geo-social networks (GeoSNs) provide context-aware services that help associate location with users and content. The proliferation of GeoSNs indicates that they're rapidly attracting users. GeoSNs currently offer different types of services, including photo sharing, friend tracking, and "check-ins. " However, this ability to reveal users' locations causes new privacy threats, which in turn call for new privacy-protection methods. The authors study four privacy aspects central to these social networks - location, absence, co-location, and identity privacy - and describe possible means of protecting privacy in these circumstances
Preserving Co-Location Privacy in Geo-Social Networks
The number of people on social networks has grown exponentially. Users share
very large volumes of personal informations and content every days. This
content could be tagged with geo-spatial and temporal coordinates that may be
considered sensitive for some users. While there is clearly a demand for users
to share this information with each other, there is also substantial demand for
greater control over the conditions under which their information is shared.
Content published in a geo-aware social networks (GeoSN) often involves
multiple users and it is often accessible to multiple users, without the
publisher being aware of the privacy preferences of those users. This makes
difficult for GeoSN users to control which information about them is available
and to whom it is available. Thus, the lack of means to protect users privacy
scares people bothered about privacy issues. This paper addresses a particular
privacy threats that occur in GeoSNs: the Co-location privacy threat. It
concerns the availability of information about the presence of multiple users
in a same locations at given times, against their will. The challenge addressed
is that of supporting privacy while still enabling useful services.Comment: 10 pages, 5 figure
Virtual Location-Based Services: Merging the Physical and Virtual World
Location-based services gained much popularity through providing users with
helpful information with respect to their current location. The search and
recommendation of nearby locations or places, and the navigation to a specific
location are some of the most prominent location-based services. As a recent
trend, virtual location-based services consider webpages or sites associated
with a location as 'virtual locations' that online users can visit in spite of
not being physically present at the location. The presence of links between
virtual locations and the corresponding physical locations (e.g., geo-location
information of a restaurant linked to its website), allows for novel types of
services and applications which constitute virtual location-based services
(VLBS). The quality and potential benefits of such services largely depends on
the existence of websites referring to physical locations. In this paper, we
investigate the usefulness of linking virtual and physical locations. For this,
we analyze the presence and distribution of virtual locations, i.e., websites
referring to places, for two Irish cities. Using simulated tracks based on a
user movement model, we investigate how mobile users move through the Web as
virtual space. Our results show that virtual locations are omnipresent in urban
areas, and that the situation that a user is close to even several such
locations at any time is rather the normal case instead of the exception
Constructing elastic distinguishability metrics for location privacy
With the increasing popularity of hand-held devices, location-based
applications and services have access to accurate and real-time location
information, raising serious privacy concerns for their users. The recently
introduced notion of geo-indistinguishability tries to address this problem by
adapting the well-known concept of differential privacy to the area of
location-based systems. Although geo-indistinguishability presents various
appealing aspects, it has the problem of treating space in a uniform way,
imposing the addition of the same amount of noise everywhere on the map. In
this paper we propose a novel elastic distinguishability metric that warps the
geometrical distance, capturing the different degrees of density of each area.
As a consequence, the obtained mechanism adapts the level of noise while
achieving the same degree of privacy everywhere. We also show how such an
elastic metric can easily incorporate the concept of a "geographic fence" that
is commonly employed to protect the highly recurrent locations of a user, such
as his home or work. We perform an extensive evaluation of our technique by
building an elastic metric for Paris' wide metropolitan area, using semantic
information from the OpenStreetMap database. We compare the resulting mechanism
against the Planar Laplace mechanism satisfying standard
geo-indistinguishability, using two real-world datasets from the Gowalla and
Brightkite location-based social networks. The results show that the elastic
mechanism adapts well to the semantics of each area, adjusting the noise as we
move outside the city center, hence offering better overall privacy
- …