# Toward Addressing Location Privacy Issues: New Affiliations with Social and Location Attributes
## 3 Results
### Identifying User's Identity--Privacy Threats in Geolocation Apps/Services
This article also discusses the development of mobile services that are equipped with a variety of different sensors, thus enabling the rise of the crowdsensing paradigm according to which workers who carry mobile devices can complete several tasks related to large-scale sensory data for traffic detection (SmartRoad), transit stations labeling (TransitLabel), air quality monitoring (Aircloud), and noise map construction (Ear-phone) [28]. Location-aware and location diversity-based offline and online crowdsensing systems are put under discussion. The authors investigate offline crowdsensing, using a combinatorial algorithm to assign tasks to workers. Then, the authors introduced online crowdsensing with dynamic workers and tasks to examine changing spatio-temporal aspects. Lyapunov optimization was incorporated to handle both stochastic characteristics and a fair allocation of worker resources. In [29], [PERSON] and [PERSON]'s focus is on re-identification trails and their potential privacy threats. Re-identification threats may arise through matching users' location information among different information bases. The study evaluates a number of information sets and links re-identification to the number of people to places. More specifically, using a generative model, [PERSON] and [PERSON] infer that \"the skew of the distribution of people to places is one of the main factors that drives trail re-identification\" (p. 413). The model under discussion estimates the risk of re-identification in case a user's information is revealed among a set of locations. Special methods and metrics are utilized so as to investigate how different location access behaviors can trace re-identification. The notion of \"re-identification\" as it was discussed in [29] is also deliberated in [30] by [PERSON], [PERSON], and [PERSON]. This paper argues that even if a user's identity is not clearly revealed, \"the geo-localized history of user-requests can act as a quasi-identifier and may be used to access sensitive information about specific individuals\" (p. 1). In other words, geolocation information is sensitive information, as it can potentially reveal the link between the real person and the pseudonym under use. Managing geolocation information that may lead to defining a user's identity is tackled in [31]. More precisely, [PERSON] and [PERSON] focus on how to employ a k-anonymity model in addressing location privacy concerns. The proposed model enables users to \"define and modify their location privacy specifications at the granularity of single messages, including the minimum anonymity level requirement, and the inaccuracy tolerances along the temporal and spatial dimensions\" (p. 17). Another important aspect of the study is the definition of the \"Restricted Space Identification\" and \"Observation Identification\" types of attack. The former addresses the possible connection among upcoming positions of the user after his identity is being revealed, and the latter refers to the matching of \"external observation on location-identity binding to a message\" (p. 2). The k-anonymity approach to location privacy addresses the de-personification procedure, using perturbation techniques, which are vital before distributing sensitive information to service providers. More precisely, users are given the opportunity to set their privacy preferences in relation to space and time to obscure their online traces. According to [PERSON], there are two distinguishable types of location-based services: personal subscriber level privacy and corporate enterprise-level privacy [32]. This tutorial introduces location l-diversity and location m-invariant in a way that is complementary to k-anonymity so as to further support location privacy in varying location privacy demands. The former refers to the level of privacy that is linked to the user's preferences, while the latter refers to the level of privacy that is imposed by the enterprise IT experts.