Transport Layer Security (TLS) is expected to transition to Post-Quantum Cryptography (PQC) to mitigate the threats posed by quantum computing. While PQC ensures long-term security, it significantly increases the TLS handshake latency due to the larger key and signature sizes. Various solutions have been proposed to address this issue; however, they suffer from limitations, such as the necessity of pre-fetching certificates or dependency on prior connections. In this paper, we propose PQTLS-AD, a novel method that leverages the Domain Name System (DNS) to efficiently distribute PQC certificates. By offloading certificate transmission to DNS, PQTLS-AD reduces handshake data overhead, effectively lowering handshake latency. We have developed a PQTLS-AD prototype and conducted extensive experiments. Our results show that PQTLS-AD significantly reduces handshake latency, cutting it by more than one Round-Trip Time (RTT) compared to standard PQTLS.

While recent advancements in deep-learning point cloud upsampling methods have improved the input to intelligent transportation systems, they still suffer from issues of domain dependency between synthetic and real-scanned point clouds. This paper addresses the above issues by proposing a new ray-based upsampling approach with an arbitrary rate, where a depth prediction is made for each query ray and its corresponding patch. Our novel method simulates the sphere-tracing ray marching algorithm on the neural implicit surface defined with an unsigned distance function (UDF) to achieve more precise and stable ray-depth predictions by training a point-transformer-based network. The rule-based mid-point query sampling method generates more evenly distributed points without requiring an end-to-end model trained using a nearest-neighbor-based reconstruction loss function, which may bias towards the training dataset. Self-supervised learning becomes possible with accurate ground truths within the input point cloud. The results demonstrate the method’s versatility across domains and training scenarios with limited computational resources and training data. Comprehensive analyses of synthetic and real-scanned applications provide empirical evidence for the significance of the upsampling task across the computer vision and graphics domains to real-world applications of ITS.

Establishing secure connections fast to end-users is crucial to online services. However, when a client sets up a TLS session with a server, the TLS handshake needs one round trip time (RTT) to negotiate a session key. Additionally, establishing a TLS session also requires a DNS lookup (e.g., the A record lookup to fetch the IP address of the server) and a TCP handshake. In this paper, we propose ZTLS to eliminate the 1-RTT latency for the TLS handshake by leveraging the DNS. In ZTLS, a server distributes TLS handshake-related data (i.e., Diffie-Hellman elements), dubbed Z-data, as DNS records. A ZTLS client can fetch Z-data by DNS lookups and derive a session key. With the session key, the client can send encrypted data along with its ClientHello, achieving 0-RTT. ZTLS supports incremental deployability on the current TLS-based infrastructure. Our prototype-based experiments show that ZTLS is 1-RTT faster than TLS in terms of the first response time.