  In the forthcoming era of 6G, the mmWave communication is envisioned to be
used in dense user scenarios with high bandwidth requirements, that necessitate
efficient and accurate beam prediction. Machine learning (ML) based approaches
are ushering as a critical solution for achieving such efficient beam
prediction for 6G mmWave communications. However, most contemporary ML
classifiers are quite susceptible to adversarial inputs. Attackers can easily
perturb the methodology through noise addition in the model itself. To mitigate
this, the current work presents a defensive mechanism for attenuating the
adversarial attacks against projected ML-based models for mmWave beam
anticipation by incorporating adversarial training. Furthermore, as training 6G
mmWave beam prediction model necessitates the use of large and comprehensive
datasets that could include sensitive information regarding the user's
location, differential privacy (DP) has been introduced as a technique to
preserve the confidentiality of the information by purposefully adding a low
sensitivity controlled noise in the datasets. It ensures that even if the
information about a user location could be retrieved, the attacker would have
no means to determine whether the information is significant or meaningless.
With ray-tracing simulations for various outdoor and indoor scenarios, we
illustrate the advantage of our proposed novel framework in terms of beam
prediction accuracy and effective achievable rate while ensuring the security
and privacy in communications.
