that the actual location of request cannot be associated (at least with a high probability) with the identity of the re-quester. To satisfy K{anonymity in LBSs, the most widely adopted anonymization strategy is cloaking. In cloaking, the actual location of request is transformed into a bounded area that is large enough to contain the requester

In this paper, we show that most of the existing k-anonymity location cloaking algorithms are concerned only and cannot effectively prevent location-dependent attacks when users' locations have Continued advances in mobile networks and positioning technologies have created a strong market push for location-based applications. Examples include location-aware emergency response, location-based advertisement, and location-based entertainment. An important challenge in the wide deployment of location-based services (LBSs) is the privacy-aware management of location information, providing Spatial cloaking algorithms can be divided into two major types: k-anonymity spatial cloak- ing [3, 4, 5, 7, 11, 2] and uncertainty spatial cloaking [1]. k-anonymity spatial cloaking aims to blur user locations into spatial regions which satisfy the user’s speci ed k-anonymity Positioning System (GPS) and location-based mobile applications. The concepts of k-anonymity, two spatial cloaking algorithms—Nearest Neighbor Cloak (NNC) and Hilbert Cloak (HC)—that utilize k-anonymity, as well as user density’s impacts on the performance are discussed in this paper. The proposed

Jan 01, 2011 · Mapping LBS k-anonymity to the above-mentioned definitions, AQ is the privacy-sensitive relation with the quasi- identifier {location}, which can be linked to location in UL. We observe that LBS k-anonymity captures the k- anonymity requirement (Definition 2) by matching at least k user locations in UL for every query’s location in AQ.

Spatial cloaking is a technique to blur a user's exact location into a spatial region in order to preserve her location privacy. The blurred spatial region must satisfy the user's specified privacy requirement. The most widely used privacy requirements are k-anonymity and minimum spatial area. Jan 01, 2014 · Therefore, most existent location cloaking research focuses on minimizing the size of the cloaked region while still satisfying the anonymity metric. To this end, a number of location cloaking algorithms have been proposed for different anonymity metrics [4, 5, 8].

Jan 01, 2014 · Therefore, most existent location cloaking research focuses on minimizing the size of the cloaked region while still satisfying the anonymity metric. To this end, a number of location cloaking algorithms have been proposed for different anonymity metrics [4, 5, 8].

In , the authors present three dynamic grid-based spatial cloaking algorithms to provide location k-anonymity and location l-diversity in a mobile environment. These algorithms rely on a semitrusted third party to give spatiotemporal cloaking. Jan 01, 2012 · firstly employed K-anonymity, which is a TTP-based ap-proach. TTP is used to blur the location information of the user. A subject is considered as K-anonymity with respect to location information, if and only if the location infor-mation sent from one mobile user is indistinguishable from the location information of at least K-1 other mobile