all InfoSec news
Differentially Private Deep Q-Learning for Pattern Privacy Preservation in MEC Offloading. (arXiv:2302.04608v1 [cs.NI])
cs.CR updates on arXiv.org arxiv.org
Mobile edge computing (MEC) is a promising paradigm to meet the quality of
service (QoS) requirements of latency-sensitive IoT applications. However,
attackers may eavesdrop on the offloading decisions to infer the edge server's
(ES's) queue information and users' usage patterns, thereby incurring the
pattern privacy (PP) issue. Therefore, we propose an offloading strategy which
jointly minimizes the latency, ES's energy consumption, and task dropping rate,
while preserving PP. Firstly, we formulate the dynamic computation offloading
procedure as a Markov decision …
applications attackers computing edge edge computing energy information iot iot applications issue latency may mobile paradigm patterns preservation privacy private quality rate requirements server service strategy task the edge