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SUMMARY:Exploiting Statistical Hardness for Increased Privacy in Wireless 
 Systems - Prof. Urbashi Mitra\, University of Southern California
DTSTART:20241209T160000Z
DTEND:20241209T170000Z
UID:TALK225061@talks.cam.ac.uk
CONTACT:Dr Amir R. Asadi
DESCRIPTION:Securing signals from unintended eavesdroppers has become an i
 ncreasingly important problem with the emergence of the Internet-of-Things
 . Herein\, we examine learning problems in signal processing that are inhe
 rently hard without key side information. In particular\, we exploit neces
 sary resolution limits for classical compressed sensing problems. To limit
  an eavesdropper's capabilities\, we create an environment for the eavesdr
 opper wherein the appropriate compressed sensing algorithm would provably 
 fail. The intended receiver overcomes this ill-posed problem by leveraging
  secret side information shared between the intended transmitter and recei
 ver. Two scenarios are considered: one for communication over a wireless c
 hannel where a novel block-sparsity based signaling strategy is employed a
 nd one for localization where novel structured noise is introduced to degr
 ade the form of the eavesdropper’s channel. In the latter scenario\, the
  transmitter designs a beamformer that introduces spurious paths\, or  alt
 ernatively spoofs the line-of-sight path\, in the channel without having a
 ccess to the channel state information. Both far-field and near-field case
 s are considered for the private localization. In both private communicati
 on and private localization\, the amount of secret information that must b
 e shared is very modest. Theoretical guarantees can be provided for both c
 ases.  Preliminary results on the information theoretic limits of this for
 m of private communication are provided. Proposed algorithms are validated
  via numerical results and it is seen that the eavesdropper’s capabiliti
 es are severely degraded.
LOCATION:MR13\, CMS Pavilion E
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