TY - GEN
T1 - On Radar Privacy in Shared Spectrum Scenarios
AU - Dimas, Anastasios
AU - Clark, Matthew A.
AU - Li, Bo
AU - Psounis, Konstantinos
AU - Petropulu, Athina P.
N1 - Publisher Copyright:
© 2019 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/5
Y1 - 2019/5
N2 - To satisfy the increasing demand for additional bandwidth from the wireless sector, regulatory bodies are considering to allow commercial wireless systems to operate on spectrum bands that until recently were reserved exclusively for military radar. Such co-existence would require mechanisms for controlling interference. One such mechanism is to assign a precoder to the communication system, which is designed to minimize the communication system's interference to the radar. This paper looks into whether the implicit radar information contained in such a precoder can be exploited by an adversary to infer the radar's location. For two specific precoder schemes, we simulate a machine learning based location inference attack. We show that the system information leaked through the precoder can indeed pose various degrees of risk to the radar's privacy, and further confirm this by computing the mutual information between the respective precoder and the radar location.
AB - To satisfy the increasing demand for additional bandwidth from the wireless sector, regulatory bodies are considering to allow commercial wireless systems to operate on spectrum bands that until recently were reserved exclusively for military radar. Such co-existence would require mechanisms for controlling interference. One such mechanism is to assign a precoder to the communication system, which is designed to minimize the communication system's interference to the radar. This paper looks into whether the implicit radar information contained in such a precoder can be exploited by an adversary to infer the radar's location. For two specific precoder schemes, we simulate a machine learning based location inference attack. We show that the system information leaked through the precoder can indeed pose various degrees of risk to the radar's privacy, and further confirm this by computing the mutual information between the respective precoder and the radar location.
KW - Machine Learning
KW - Null space precoding
KW - Radar Privacy
KW - Spectrum co-existence
KW - Spectrum sharing
UR - http://www.scopus.com/inward/record.url?scp=85069005293&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069005293&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2019.8682745
DO - 10.1109/ICASSP.2019.8682745
M3 - Conference contribution
AN - SCOPUS:85069005293
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 7790
EP - 7794
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Y2 - 12 May 2019 through 17 May 2019
ER -