TY - GEN
T1 - Enhancing privacy through caching in location-based services
AU - Niu, Ben
AU - Li, Qinghua
AU - Zhu, Xiaoyan
AU - Cao, Guohong
AU - Li, Hui
N1 - Publisher Copyright:
© 2015 IEEE.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2015/8/21
Y1 - 2015/8/21
N2 - Privacy protection is critical for Location-Based Services (LBSs). In most previous solutions, users query service data from the untrusted LBS server when needed, and discard the data immediately after use. However, the data can be cached and reused to answer future queries. This prevents some queries from being sent to the LBS server and thus improves privacy. Although a few previous works recognize the usefulness of caching for better privacy, they use caching in a pretty straightforward way, and do not show the quantitative relation between caching and privacy. In this paper, we propose a caching-based solution to protect location privacy in LBSs, and rigorously explore how much caching can be used to improve privacy. Specifically, we propose an entropy-based privacy metric which for the first time incorporates the effect of caching on privacy. Then we design two novel caching-aware dummy selection algorithms which enhance location privacy through maximizing both the privacy of the current query and the dummies' contribution to cache. Evaluations show that our algorithms provide much better privacy than previous caching-oblivious and caching-aware solutions.
AB - Privacy protection is critical for Location-Based Services (LBSs). In most previous solutions, users query service data from the untrusted LBS server when needed, and discard the data immediately after use. However, the data can be cached and reused to answer future queries. This prevents some queries from being sent to the LBS server and thus improves privacy. Although a few previous works recognize the usefulness of caching for better privacy, they use caching in a pretty straightforward way, and do not show the quantitative relation between caching and privacy. In this paper, we propose a caching-based solution to protect location privacy in LBSs, and rigorously explore how much caching can be used to improve privacy. Specifically, we propose an entropy-based privacy metric which for the first time incorporates the effect of caching on privacy. Then we design two novel caching-aware dummy selection algorithms which enhance location privacy through maximizing both the privacy of the current query and the dummies' contribution to cache. Evaluations show that our algorithms provide much better privacy than previous caching-oblivious and caching-aware solutions.
UR - http://www.scopus.com/inward/record.url?scp=84953810577&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84953810577&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM.2015.7218474
DO - 10.1109/INFOCOM.2015.7218474
M3 - Conference contribution
AN - SCOPUS:84953810577
T3 - Proceedings - IEEE INFOCOM
SP - 1017
EP - 1025
BT - 2015 IEEE Conference on Computer Communications, IEEE INFOCOM 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015
Y2 - 26 April 2015 through 1 May 2015
ER -