Affiliation:
1. University of Minnesota, Minneapolis, MN
2. Purdue University, West Lafayette, IN
Abstract
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's cloaked location rather than the exact location. Our proposed
privacy-aware query processor
is completely independent of how we compute the user's cloaked location. In other words, any existing location anonymization algorithms that blur the user's private location into cloaked rectilinear areas can be employed to protect the user's 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.
Funder
Division of Computer and Network Systems
Division of Information and Intelligent Systems
Publisher
Association for Computing Machinery (ACM)
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