TY - GEN
T1 - Your actions tell where you are
T2 - 3rd IEEE International Conference on Communications and Network Security, CNS 2015
AU - Zhang, Jinxue
AU - Sun, Jingchao
AU - Zhang, Rui
AU - Zhang, Yanchao
N1 - Funding Information:
This work is partially supported by ARO through W911NF-15-1-0328.
Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/3
Y1 - 2015/12/3
N2 - Twitter is an extremely popular social networking platform. Most Twitter users do not disclose their locations due to privacy concerns. Although inferring the location of an individual Twitter user has been extensively studied, it is still missing to effectively find the majority of the users in a specific geographical area without scanning the whole Twittersphere, and obtaining these users will result in both positive and negative significance. In this paper, we propose LocInfer, a novel and lightweight system to tackle this problem. LocInfer explores the fact that user communications in Twitter exhibit strong geographic locality, which we validate through large-scale datasets. Based on the experiments from four representative metropolitan areas in U.S., LocInfer can discover on average 86.6% of the users with 73.2% accuracy in each area by only checking a small set of candidate users. We also present a countermeasure to the users highly sensitive to location privacy and show its efficacy by simulations.
AB - Twitter is an extremely popular social networking platform. Most Twitter users do not disclose their locations due to privacy concerns. Although inferring the location of an individual Twitter user has been extensively studied, it is still missing to effectively find the majority of the users in a specific geographical area without scanning the whole Twittersphere, and obtaining these users will result in both positive and negative significance. In this paper, we propose LocInfer, a novel and lightweight system to tackle this problem. LocInfer explores the fact that user communications in Twitter exhibit strong geographic locality, which we validate through large-scale datasets. Based on the experiments from four representative metropolitan areas in U.S., LocInfer can discover on average 86.6% of the users with 73.2% accuracy in each area by only checking a small set of candidate users. We also present a countermeasure to the users highly sensitive to location privacy and show its efficacy by simulations.
UR - http://www.scopus.com/inward/record.url?scp=84966270832&partnerID=8YFLogxK
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U2 - 10.1109/CNS.2015.7346854
DO - 10.1109/CNS.2015.7346854
M3 - Conference contribution
AN - SCOPUS:84966270832
T3 - 2015 IEEE Conference on Communications and NetworkSecurity, CNS 2015
SP - 424
EP - 432
BT - 2015 IEEE Conference on Communications and NetworkSecurity, CNS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 September 2015 through 30 September 2015
ER -