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The widespread use of location-based services (LBSs).allowing untrusted service providers to collect large quantities of information regarding users locations.has raised serious privacy concems As a response to these issues.a variety of LBS privacy protectionmechanisms (LPPMs) have been proposed recentlv However.the evaluation for them remains problematicbecause of the absence of a generic adversarial model for most of the existing privacy metrics In particular.the relationshipsbetween these metrics have not been examined in depth under a common adversarial model.leading to a possible selection ofthe inappropriate metric.with the risk of wrong evaluation for LPPMs.In this paper.we address these issues by proposing a pnvacy quantifiationmodelbased on Bavesian conditional privacy for specifying the general adversarial model It employs a general definition of conditional privacv metric wrt the adversarvs estimation error to compare the different LBS privacy metncs Moreover. we present a theoretical analvsis for specifying how to connect our metric with other popular LBS privacy metrics We show that our pnvacy quantification model permits interpreting and comparing various popular LBS privacy metrics under a common perspective Our results contribute to a better understanding of how privacy properties can be measure. and to the better selection of the most appropnate metric for a given LBS application.