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While providing unprecedented convenience to peo- ple’s daily life, location-based services (LBSs) may cause se- rious concerns on users’ location privacy when the system is compromised. Although various location privacy protection mechanisms have been developed for LBSs, the ambient physical environment often imposes some fundamental limitations on their performances. As a result, mobile users may experience a spatial diversity in the achievable location privacy when traveling along their routes. However, to the best of our knowledge, an appropriate location privacy metric that can capture the influence of the ambient environment is still missing in the literature. Also, none of the existing location privacy protection methods can properly leverage such spatial diversity. With this consideration, new ambient environment-dependent location privacy metrics are proposed in this work, together with a stochastic model that can capture their spatial variations along the user’s route. Based on this modeling, a new optimal stopping based LBS access scheme that allows mobile users to fully leverage the spatial diversity and achieve a substantially better performance is developed. The effectiveness of the proposed scheme is corroborated by both numerical results and simulations over real-world road maps.
A. Ambient Environment-Dependent Privacy Metrics As discussed previously, the privacy loss function l(x;M) should capture the fundamental impact on user’s privacy imposed by the ambient environment. To this end, three road structure based privacy metrics are proposed in this subsec- tion, serving as concrete examples of l(x;M) in the context of vehicular network LBSs. To the best of our knowledge, this work is among the first to study such ambient environment- dependent privacy metrics.
Area based privacy metric l A : The essential idea of many existing location privacy protection mechanisms is to generate dummy users at locations different from the true user location. Apparently, when more candidate locations are available, bet- ter privacy protection can be achieved. For LBSs in vehicular networks, since candidate locations of the dummy users cannot be off the roads, a set of roads occupying a smaller area usually implies a potentially more severe privacy breach.
With the model presented in the previous section, an optimal stopping based privacy-aware LBS access scheme that allows the mobile user to fully leverage the spatial diversity of location privacy is developed in this section. Before presenting the details, some intuitions are provided first to facilitate the understanding of the proposed scheme.
In practice, when encountering an AP at time t i , the mobile user will compare the actual reward f t i (r t i ) of accessing the LBS immediately with the best possible expected actual reward of accessing the LBS in the future. Clearly, the user will skip the current AP and access the LBS in the future if and only if the reward of the latter option is larger.
With the rapid development of wireless communication tech- nology and mobile devices, location-based service (LBS) has emerged as a promising way of improving our quality of life . In LBS systems, mobile users can send location dependent service requests to the LBS providers through nearby wireless access points (APs). For example, a driver may leverage nearby roadside WiFi APs 1 for bandwidth de- manding LBSs
(e.g., multimedia-based tourism and restaurant recommendations). However, the information contained in the LBS requests and that related to the corresponding APs may breach the users’ location privacy when the LBS server and the APs are compromised by the adversary. Considering this, various location privacy protection mechanisms have been developed in the literature for LBSs.
• New location privacy metrics that can incorporate the im- pact of the ambient environment on privacy are proposed. Also, a reflected random walk based stochastic model is developed to capture the spatial variation of these privacy metrics, along with the corresponding parameter estimation methods.
• A novel and provably convergent optimal stopping algo- rithm is developed to allow the mobile users to adequately take spatial diversity into consideration for better privacy protection when making the LBS access decision. • The effectiveness of the proposed scheme is validated through both a numerical example and simulations over real-world road maps.
In this work, three new location privacy metrics that can capture the influence on privacy of the ambient environment are proposed. In addition, a stochastic model based on reflected random walk is developed to characterize the spatial variation of the location privacy along the user’s route. Based on this modeling, a new optimal stopping based privacy-aware LBS access algorithm that allows the mobile users to fully leverage the spatial diversity of location privacy is developed.
Corre- sponding analysis shows that the optimal stopping decision and values of the privacy-aware LBS access problem can be obtained through iterated computations. Results of both numerical and real-world examples show that the proposed scheme can achieve a significantly better performance as compared to the baseline approach.