Location-based services (LBS) provide specific personalized services based on the location information provided by the user. Disclosing the users' private data is a challenging issue prevailing in LBS. Users become victims of online user-profile disclosure risks when they continuously utilize the services and attempt to disclose private data. The proposed dispersed dummy selection-based approaches such as dispersed dummy selection including actual location (DDSIA) and dispersed dummy selection excluding actual location (DDSEA) avert user-profiling issue. The dummy locations are chosen based on the auxiliary information of the location, user, and physical dispersion. The auxiliary information about the location is fetched from the local fog servers, and auxiliary information about the user is stored and retrieved from the cloud storage. An anonymous circular area is considered to ensure the dummy locations are dispersed, and the user-location correlations get dissipated. The detailed analysis of the proposed approaches is explored with the real-world map data, and its effectiveness is verified with simulations using Matlab. In addition, the qualitative analysis tests the resistance of the dummies against human observations. The results obtained substantiate that the proposed approaches are efficient in terms of dispersion degree and resistance to human observations that avert user profiling.
With advancements in smart mobile devices and their capabilities, location-based services have gained utmost importance, as its individual and social benefits are enormous. Users of location-based services have a concern to the security issues posed by its usage as the location service providers track the users' interests, behavior, and identity information. Most of the location-based services are launched from mobile phones that have stringent resources; hence incorporating encryption schemes becomes tedious, and further, dual identity attacks uncover the encrypted message. A fog-assisted privacy protection scheme for location-based service (FPriLBS) employs a semi-trusted third party as a fog server to eliminate redundant queries submitted to the location service provider in addition to the trusted helper selection scheme which hides the real identity of the user from the fog server. The experimental results show that the proposed FPriLBS outperforms the existing schemes in terms of processing time and processing cost.
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