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.

a cloaking area. We say a cloaking area is a K-anonymity area (KAA) if its entropy exceeds the level that is required for K-anonymity protection. With this definition in place, we then consider how to compute a user’s cloaking area. For anonymity protection, each cloaking area needs to be a KAA, where the value of K is user-defined. 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 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

Nov 21, 2007 · This architecture includes the development of a personalized location anonymization model and a suite of location perturbation algorithms. A unique characteristic of our location privacy architecture is the use of a flexible privacy personalization framework to support location k-anonymity for a wide range of mobile clients with context

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. 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.

k-anonymity is a property possessed by certain anonymized data.The concept of k-anonymity was first introduced by Latanya Sweeney and Pierangela Samarati in a paper published in 1998 as an attempt to solve the problem: "Given person-specific field-structured data, produce a release of the data with scientific guarantees that the individuals who are the subjects of the data cannot be re

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