TY - GEN
T1 - Location Privacy in Mobile Edge Clouds
AU - He, Ting
AU - Ciftcioglu, Ertugrul N.
AU - Wang, Shiqiang
AU - Chan, Kevin S.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/13
Y1 - 2017/7/13
N2 - In this paper, we consider user location privacy in mobile edge clouds (MECs). MECs are small clouds deployed at the network edge to offer cloud services close to mobile users, and many solutions have been proposed to maximize service locality by migrating services to follow their users. Co-location of a user and his service, however, implies that a cyber eavesdropper observing service migrations between MECs can localize the user up to one MEC coverage area, which can be fairly small (e.g., a femtocell). We consider using chaff services to defend against such an eavesdropper, with focus on strategies to control the chaffs. Assuming the eavesdropper performs maximum likelihood (ML) detection, we consider both heuristic strategies that mimic the user's mobility and optimized strategies designed to minimize the detection or tracking accuracy. We show that a single chaff controlled by the optimal strategy can drive the eavesdropper's tracking accuracy to zero when the user's mobility is sufficiently random. The efficacy of our solutions is verified through extensive simulations.
AB - In this paper, we consider user location privacy in mobile edge clouds (MECs). MECs are small clouds deployed at the network edge to offer cloud services close to mobile users, and many solutions have been proposed to maximize service locality by migrating services to follow their users. Co-location of a user and his service, however, implies that a cyber eavesdropper observing service migrations between MECs can localize the user up to one MEC coverage area, which can be fairly small (e.g., a femtocell). We consider using chaff services to defend against such an eavesdropper, with focus on strategies to control the chaffs. Assuming the eavesdropper performs maximum likelihood (ML) detection, we consider both heuristic strategies that mimic the user's mobility and optimized strategies designed to minimize the detection or tracking accuracy. We show that a single chaff controlled by the optimal strategy can drive the eavesdropper's tracking accuracy to zero when the user's mobility is sufficiently random. The efficacy of our solutions is verified through extensive simulations.
UR - http://www.scopus.com/inward/record.url?scp=85027244079&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027244079&partnerID=8YFLogxK
U2 - 10.1109/ICDCS.2017.39
DO - 10.1109/ICDCS.2017.39
M3 - Conference contribution
AN - SCOPUS:85027244079
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 2264
EP - 2269
BT - Proceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017
A2 - Lee, Kisung
A2 - Liu, Ling
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017
Y2 - 5 June 2017 through 8 June 2017
ER -