A privacy-aware visual query approach for location-based data

Abstract

Visual querying of location-based data assists users in expressing query requirements, investigating query results and making inferences. However, directly accessing data records exposes individual location information and may cause privacy issues. Conventional aggregation-based methods can preserve location-relevant privacy but may lead to the loss of detailed information and failure of analysis. Visualization aids users in gaining a deeper comprehension of the query process and the variation of information concerning privacy-preservation. In this paper, we present a privacy-aware visual query approach for location-based data. We propose a graph-based privacy-preserving scheme to protect location privacy in the visualization, and two visual metaphors to enhance understandings of information-variation in the privacy-preserving process. We design and implement a visual interface that supports a progressive process of query conditions specification and query results exploration. Experiments on real-world urban datasets demonstrate that our approach is capable of making a fair balance between location privacy and data analysis.