A New Cloaking Method Supporting both K-anonymity and L-diversity for Privacy Protection in Location-Based Service

Abstract

In Location-Based Services (LBSs), users send location-based queries to LBS servers along with their exact locations, but the location information of the users can be misused by adversaries. In this regard, there must be a mechanism which can deal with the privacy protection of the users. In this paper, we propose a cloaking method considering both K-anonymity and L-diversity. Our cloaking method creates a minimum cloaking region by finding L number of buildings (L-diversity) and then finds K number of users (K-anonymity). To support it, we use R*-tree based index structures as well as efficient filtering techniques to generate a minimum cloaking region. Finally, we show from our performance analysis that our cloaking method outperforms the existing grid-based cloaking method in terms of the size of cloaking regions and cloaking region creation time.