Future automotive architectures are expected to transition from a network-centric to a domain-centered architecture featuring central compute units. Powerful domain controllers or smart sensors alleviate the load on these central units and communication systems. These controllers execute tasks with varying criticalities on heterogeneous multicore processors, and are ideally capable of dynamically balancing the computing load between the central unit and sensors. Here, Artificial Intelligence (AI) capabilities playa crucial role, as it is in high demand for such an automotive architecture. However, AI still requires specialized accelerators to improve their computation performance. Task-oriented distributed computing with criticalities up to ASIL-D necessitates the development and utilization of specialized methodologies, such as safety, through the isolation and abstraction of low-level hardware concepts. Meanwhile, online monitoring and diagnostics become vital features to detect errors during operation. The EMDRIVE architecture includes methods, components, and strategies to enhance the performance, safety, and security of such distributed computing platforms. The nationally funded EMDRIVE project connects its twelve partners from academia and industry and is currently in its intermediate stage.

Maintaining privacy on the Internet with the presence of powerful adversaries such as nation-state attackers is a challenging topic, and the Tor project is currently the most important tool to protect against this threat. The circuit construction protocol (CCP) negotiates cryptographic keys for Tor circuits, which overlay TCP/IP by routing Tor cells over n onion routers. The current circuit construction protocol provides strong security guarantees such as forward secrecy by exchanging 𝒪(n2) messages.