son. Location privacy based on k-anonymity addresses this threat by cloaking the person’s location such that there are at least k−1 other people within the cloaked area. We pro-pose a distributed approach that integrates nicely with exist-ing infrastructures for location-based services, as opposed to previous work.

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 location anonymization on mobile users’ LBS request mes-sages, such as identity removal and spatio-temporal cloak-ing of location information. We develop a suite of scalable and yet efficient spatio-temporal cloaking algorithms, called CliqueCloakalgorithms, to provide high quality personal-ized location k-anonymity, aiming at avoiding or Spatial cloaking is a privacy mechanism that is used to satisfy specific privacy requirements by blurring users’ exact locations into cloaked regions. This technique is usually integrated into applications in various environments to minimize the disclosure of private information when users request location-based service. k-anonymity. 1.2 Related Work While significant research has gone into algorithms that enforce k-anonymity [1, 5, 6, 7], very few of them address historical k-anonymity. Gruteser and Liu specifically investigate privacy issues in continuous LBS [8]. They introduce the location inference problem where an adversary can infer supposedly hidden lo- In this work, k-anonymity is employed as the PPDM technique. Once k-anonymity with k=2 is applied to the input dataset, the resulting anonymized dataset becomes input for 6 machine learning algorithms: 1)artificial neural network (ANN), 2) C4.5 decision tree, 3) decision stump algorithm, 4) classification and cloaking area. Researchers have proposed various algorithms to find cloaking areas for a given user location and a K-anonymity requirement. 2.3 Spatial and Temporal Cloaking The problem of location depersonalization on dynamic data was studied by Gruteser and Grunwald (10). The authors in this work introduced middleware architecture, with the

gether. [9] identifies that location cloaking algorithms with only k-anonymity and l-diversity guarantee are not effective for continuous LBS and therefore propose query m-invariance as a necessary criterion when dealing with continuous location queries. However, m-invariance based approach is ineffective when the mobile users

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

which provides location k-anonymity for mobile users of a LBS provider. The cloaking algorithm is run by the location protection broker on a trusted server. It anonymizes messages from the mobile nodes by cloaking the location information contained in the messages to reduce or avoid privacy threats before forwarding them to the LBS providers.

The main idea is to expand the MBV (minimum bounding volume) to a three-dimensional space, thus for a user who initiated location services can find k-anonymity cloaking set in the three-dimensional space. The efficiency and effectiveness of the proposed 3d Clique Cloak algorithm are validated by series of carefully designed experiments. spatial cloaking and temporal cloaking. 1.3 k-Anonymity and Location k-Anonymity There are two popular approaches to protect location privacy in the context of LBS usage: policy-based [9] and anonymity-based approaches [8]. In policy-based ap-proaches, mobile clients specify their location privacy 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 Gruteser and Grunwald propose the concept of location k-anonymity. K-anonymity requires that when a user sends a location request data to a LBSPs, the cloaking region in which a query user is located must contain at least the other users, so that the probability that the location query user is identified does not exceed 1/k. k-anonymity for a snapshot of the database. In LBS, the user location is continuously changing. Such dynamic be-havior calls for continuous maintenance of the k-anonymity model. (4) These approaches assume a unifiedk-anonymity requirement for all the stored records. In our P2P spatial cloaking algorithm, k-anonymity is a user-specified privacy the location now contains k users who all look identical. This protects a user’s identity and hence privacy as an attacker is unable to distinguish between any of the k users. We review two location privacy k-Anonymity algorithms and nd very di erent complexities; one claims to be linear in the degree of anonymity, k, while the other is NP-hard. Second, it provides fast and effective location cloaking algorithms for location k-anonymity and location l-diversity in a mobile environment. We develop dynamic bottom- up and top-down grid