  To accommodate heterogeneous tasks in Internet of Things (IoT), a new
communication and computing paradigm termed mobile edge computing emerges that
extends computing services from the cloud to edge, but at the same time exposes
new challenges on security. The present paper studies online security-aware
edge computing under jamming attacks. Leveraging online learning tools, novel
algorithms abbreviated as SAVE-S and SAVE-A are developed to cope with the
stochastic and adversarial forms of jamming, respectively. Without utilizing
extra resources such as spectrum and transmission power to evade jamming
attacks, SAVE-S and SAVE-A can select the most reliable server to offload
computing tasks with minimal privacy and security concerns. It is analytically
established that without any prior information on future jamming and server
security risks, the proposed schemes can achieve ${\cal O}\big(\sqrt{T}\big)$
regret. Information sharing among devices can accelerate the security-aware
computing tasks. Incorporating the information shared by other devices, SAVE-S
and SAVE-A offer impressive improvements on the sublinear regret, which is
guaranteed by what is termed "value of cooperation." Effectiveness of the
proposed schemes is tested on both synthetic and real datasets.
