97 research outputs found
When the Hammer Meets the Nail: Multi-Server PIR for Database-Driven CRN with Location Privacy Assurance
We show that it is possible to achieve information theoretic location privacy
for secondary users (SUs) in database-driven cognitive radio networks (CRNs)
with an end-to-end delay less than a second, which is significantly better than
that of the existing alternatives offering only a computational privacy. This
is achieved based on a keen observation that, by the requirement of Federal
Communications Commission (FCC), all certified spectrum databases synchronize
their records. Hence, the same copy of spectrum database is available through
multiple (distinct) providers. We harness the synergy between multi-server
private information retrieval (PIR) and database- driven CRN architecture to
offer an optimal level of privacy with high efficiency by exploiting this
observation. We demonstrated, analytically and experimentally with deployments
on actual cloud systems that, our adaptations of multi-server PIR outperform
that of the (currently) fastest single-server PIR by a magnitude of times with
information theoretic security, collusion resiliency, and fault-tolerance
features. Our analysis indicates that multi-server PIR is an ideal
cryptographic tool to provide location privacy in database-driven CRNs, in
which the requirement of replicated databases is a natural part of the system
architecture, and therefore SUs can enjoy all advantages of multi-server PIR
without any additional architectural and deployment costs.Comment: 10 pages, double colum
Domain-Adaptive Device Fingerprints for Network Access Authentication Through Multifractal Dimension Representation
RF data-driven device fingerprinting through the use of deep learning has
recently surfaced as a potential solution for automated network access
authentication. Traditional approaches are commonly susceptible to the domain
adaptation problem where a model trained on data from one domain performs badly
when tested on data from a different domain. Some examples of a domain change
include varying the device location or environment and varying the time or day
of data collection. In this work, we propose using multifractal analysis and
the variance fractal dimension trajectory (VFDT) as a data representation input
to the deep neural network to extract device fingerprints that are domain
generalizable. We analyze the effectiveness of the proposed VFDT representation
in detecting device-specific signatures from hardware-impaired IQ signals, and
evaluate its robustness in real-world settings, using an experimental testbed
of 30 WiFi-enabled Pycom devices under different locations and at different
scales. Our results show that the VFDT representation improves the scalability,
robustness and generalizability of the deep learning models significantly
compared to when using raw IQ data
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