Optimal location query based on k nearest neighbours

Optimal location query in road networks is a basic operation in the location intelligence applications.Given a set of clients and servers on a road network,the purpose of optimal location query is to obtain a location for a new server,so that a certain objective function calculated based on the locations of clients and servers is optimal.Existing works assume no labels for servers and that a client only visits the nearest server.These assumptions are not realistic and it renders the existing work not useful in many cases.In this paper,we relax these assumptions and consider the k nearest neighbours(KNN)of clients.We introduce the problem of KNN-based optimal location query(KOLQ)which considers the k nearest servers of clients and labeled servers.We also introduce a variant problem called relocation KOLQ(RKOLQ)which aims at relocating an existing server to an optimal location.Two main analysis algorithms are proposed for these problems.Extensive experiments on the real road networks illustrate the efficiency of our proposed solutions.

Achieve Personalized Anonymity Through Query Blocks Exchanging

In cyberspace security,the privacy in location-based services(LBSs) becomes more critical. In previous solutions,a trusted third party(TTP) was usually employed to provide disturbance or obfuscation,but it may become the single point of failure or service bottleneck. In order to cope with this drawback,we focus on another important class,establishing anonymous group through short-range communication to achieve k-anonymity with collaborative users. Along with the analysis of existing algorithms,we found users in the group must share the same maximum anonymity degree,and they could not ease the process of preservation in a lower one. To cope with this problem,we proposed a random-QBE algorithm to put up with personalized anonymity in user collaboration algorithms,and this algorithm could preserve both query privacy and location privacy. Then we studied the attacks from passive and active adversaries and used entropy to measure user's privacy level. Finally,experimental evaluations further verify its effectiveness and efficiency.

DISTANCE-BASED UPDATE STRATEGY IN LDCQ

A new update strategy, distance-based update strategy, is presented in Location Dependent Continuous Query (LDCQ) under error limitation. There are different possibilities to intersect when the distances between moving objects and the querying boundary are different.Therefore, moving objects have different influences to the query result. We set different deviation limits for different moving objects according to distances. A great number of unnecessary updates are reduced and the payload of the system is relieved.

An efficient method for privacy preserving location queries

Recently, the issue of privacy preserving loca- tion queries has attracted much research. However, there are few works focusing on the tradeoff between location privacy preservation and location query information collection. To tackle this kind of tradeoff, we propose the privacy persevering location query （PLQ）, an efficient privacy pre-serving location query processing framework. This frame- work can enable the location-based query without revealing user location information. The framework can also facilitate location-based service providers to collect some information about the location based query, which is useful in practice. PLQ consists of three key components, namely, the location anonymizer at the client side, the privacy query processor at the server side, and an additional trusted third party connect- ing the client and server. The location anonymizer blurs the user location into a cloaked area based on a map-hierarchy. The map-hierarchy contains accurate regions that are parti- tioned according to real landforms. The privacy query pro- cessor deals with the requested nearest-neighbor （NN） loca- tion based query. A new convex hull of polygon （CHP） algo- rithm is proposed for nearest-neighbor queries using a poly- gon cloaked area. The experimental results show that our al- gorithms can efficiently process location based queries.