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In this work, we establish a physical access control mechanism for vehicular platoons. The goal is to restrict vehicle-to-vehicle (V2V) communications to platooning members by tying the digital identity of a candidate vehicle requesting to join a platoon to its physical trajectory relative to the platoon. We propose the Wiggle protocol that employs a physical challenge-response exchange to prove that a candidate requesting to be admitted into a platoon actually follows it. The protocol name is inspired by the random longitudinal movements that the candidate is challenged to execute. Wiggle prevents any remote adversary from joining the platoon and injecting fake V2V messages. Compared to prior works, Wiggle is resistant to prerecording attacks and can verify that the candidate is traveling behind the verifier in the same lane. 

Artificial intelligence nowadays plays an increasingly prominent role in our life since decisions that were once made by humans are now delegated to automated systems. A machine learning algorithm trained based on biased data, however, tends to make unfair predictions. Developing classification algorithms that are fair with respect to protected attributes of the data thus becomes an important problem. Motivated by concerns surrounding the fairness effects of sharing and few-shot machine learning tools, such as the Model Agnostic Meta-Learning [1] framework, we propose a novel fair fast-adapted few-shot meta-learning approach that efficiently mitigates biases during meta train by ensuring controlling the decision boundary covariance that between the protected variable and the signed distance from the feature vectors to the decision boundary. Through extensive experiments on two real-world image benchmarks over three state-of-the-art meta-learning algorithms, we empirically demonstrate that our proposed approach efficiently mitigates biases on model output and generalizes both accuracy and fairness to unseen tasks with a limited amount of training samples.