Secure Massive IoT Using Hierarchical Fast Blind Deconvolution. (arXiv:1801.09628v2 [cs.IT] CROSS LISTED)

The Internet of Things and specifically the Tactile Internet give rise to
significant challenges for notions of security. In this work, we introduce a
novel concept for secure massive access. The core of our approach is a fast and
low-complexity blind deconvolution algorithm exploring a bi-linear and
hierarchical compressed sensing framework. We show that blind deconvolution has
two appealing features: 1) There is no need to coordinate the pilot signals, so
even in the case of collisions in user activity, the information messages can
be resolved. 2) Since all the individual channels are recovered in parallel,
and by assumed channel reciprocity, the measured channel entropy serves as a
common secret and is used as an encryption key for each user. We will outline
the basic concepts underlying the approach and describe the blind deconvolution
algorithm in detail. Eventually, simulations demonstrate the ability of the
algorithm to recover both channel and message. They also exhibit the inherent
trade-offs of the scheme between economical recovery and secret capacity.

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