Location Prediction Based on Transition Probability Matrices Constructing from Sequential Rules for Spatial-Temporal K-Anonymity Dataset.

ABSTRACT: Spatial-temporal k-anonymity has become a mainstream approach among techniques for protection of users' privacy in location-based services (LBS) applications, and has been applied to several variants such as LBS snapshot queries and continuous queries. Analyzing large-scale spatial-temporal anonymity sets may benefit several LBS applications. In this paper, we propose two location prediction methods based on transition probability matrices constructing from sequential rules for spatial-temporal k-anonymity dataset. First, we define single-step sequential rules mined from sequential spatial-temporal k-anonymity datasets generated from continuous LBS queries for multiple users. We then construct transition probability matrices from mined single-step sequential rules, and normalize the transi

SUBMITTER: Zhang H 

PROVIDER: S-EPMC4980015 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature