Semantic analysis in location privacy preserving

Abstract

SUMMARYWith the increasing use of location-based services, location privacy has recently started raising serious concerns. Location perturbation and obfuscation are most widely used for location privacy preserving. To protect a user from being identified, a cloaked spatial region that contains other k 1 nearest neighbors of the user is submitted to the location-based service provider, instead of the accurate position. In this paper, we consider the location-aware applications that services are different among regions. In such scenarios, the semantic distance between users should be considered besides the Euclidean distance for searching the neighbors of a user. We define a novel distance measurement that combines the semantic and the Euclidean distance to address the privacy-preserving issue in the aforementioned applications. We also present an algorithm kNNH to implement our proposed method. Moreover, we conduct performance study experiments on the proposed algorithm. The experimental results further suggest that the proposed distance metric and the algorithm can successfully retain the utility of the location services while preserving users' privacy. Copyright

Securing cyberspace

“…In , the authors proposed an algorithm to address the problem of preserving privacy for individual users in location‐aware applications. They define a novel distance measurement that combines the semantic and Euclidean distance to address the privacy‐preserving issue.…”

Covert channels in modern computer systems : the cases of mobile and cloud

“…Location tracking has achieved significant attention in the research community [44,96,73,67,92] and great notoriety in the news cycle. In late 2017, Quartz [16] reported that Android devices sent location data to Google when they were within range of a new cell tower.…”