  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 or its online variation can drive the eavesdropper's tracking
accuracy to zero when the user's mobility is sufficiently random. We further
propose extended strategies that utilize randomization to defend against an
advanced eavesdropper aware of the strategy. The efficacy of our solutions is
verified through both synthetic and trace-driven simulations.
