TY - GEN
T1 - CLPP
T2 - IEEE International Conference on Communications, ICC 2015
AU - Zhang, Hongli
AU - Xu, Zhikai
AU - Zhou, Zhigang
AU - Shi, Jiantao
AU - Du, Xiaojiang
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/9
Y1 - 2015/9/9
N2 - Location-based social network (LBSN) has grown exponentially over the past several years. Given its high utility value, LBSN, however, has raised serious concerns about users' location privacy. Although users may avoid releasing geo-content in sensitive locations, this, however, does not necessarily prevent the adversary from inferring users' privacy through spatial-temporal correlations and historical information. In this paper, we introduce a new location privacy problem: context-aware location privacy protection (CLPP) problem where the privacy requirements of users are not constant and isolated. We propose a novel metric to quantify the privacy risks. Then the CLPP is formalized as how to accurately and efficiently evaluate whether the users' published geo-content meet the user's privacy requirement. To achieve online evaluating, we design two novel algorithms to calculate the correlation between the locations. Eventually, our experimental results demonstrate the validity and practicality of the proposed strategy.
AB - Location-based social network (LBSN) has grown exponentially over the past several years. Given its high utility value, LBSN, however, has raised serious concerns about users' location privacy. Although users may avoid releasing geo-content in sensitive locations, this, however, does not necessarily prevent the adversary from inferring users' privacy through spatial-temporal correlations and historical information. In this paper, we introduce a new location privacy problem: context-aware location privacy protection (CLPP) problem where the privacy requirements of users are not constant and isolated. We propose a novel metric to quantify the privacy risks. Then the CLPP is formalized as how to accurately and efficiently evaluate whether the users' published geo-content meet the user's privacy requirement. To achieve online evaluating, we design two novel algorithms to calculate the correlation between the locations. Eventually, our experimental results demonstrate the validity and practicality of the proposed strategy.
KW - inference attack
KW - location privacy
KW - location-based social network
KW - privacy preserving
UR - http://www.scopus.com/inward/record.url?scp=84953724288&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84953724288&partnerID=8YFLogxK
U2 - 10.1109/ICC.2015.7248480
DO - 10.1109/ICC.2015.7248480
M3 - Conference contribution
AN - SCOPUS:84953724288
T3 - IEEE International Conference on Communications
SP - 1164
EP - 1169
BT - 2015 IEEE International Conference on Communications, ICC 2015
Y2 - 8 June 2015 through 12 June 2015
ER -