Casper: Query processing for location services without compromising privacy

In this article, we present a new privacy-aware query processing framework, Capser, in which mobile and stationary users can obtain snapshot and/or continuous location-based services without revealing their private location information. In particular, we propose a privacy-aware query processor embedded inside a location-based database server to deal with snapshot and continuous queries based on the knowledge of the user\u27s cloaked location rather than the exact location. Our proposed privacy-aware query processor is completely independent of how we compute the user\u27s cloaked location. In other words, any existing location anonymization algorithms that blur the user\u27s private location into cloaked rectilinear areas can be employed to protect the user\u27s location privacy. We first propose a privacy-aware query processor that not only supports three new privacy-aware query types, but also achieves a trade-off between query processing cost and answer optimality. Then, to improve system scalability of processing continuous privacy-aware queries, we propose a shared execution paradigm that shares query processing among a large number of continuous queries. The proposed scalable paradigm can be tuned through two parameters to trade off between system scalability and answer optimality. Experimental results show that our query processor achieves high quality snapshot and continuous location-based services while supporting queries and/or data with cloaked locations