Advances in Secure Communication and Computation

The field of secure communication and computation is rapidly advancing with innovative solutions to longstanding problems. Recent developments have focused on enhancing the scalability and practical security of large-scale quantum key distribution networks, accelerating graph neural networks, and improving the efficiency of fully homomorphic encryption. Notably, researchers have proposed zero-trust relay architectures, adaptive edge sampling strategies, and hybrid CPU-GPU systems to address the limitations of existing methods. These advancements have significant implications for the integration of secure communication and computation into real-world systems. Noteworthy papers include:

  • A zero-trust relay design that applies fully homomorphic encryption to perform intermediate OTP re-encryption without exposing plaintext keys.
  • AES-SpMM, an adaptive edge sampling SpMM kernel that balances accuracy and speed in graph neural networks.
  • PilotANN, a hybrid CPU-GPU system for graph-based approximate nearest neighbor search that achieves significant speedup and memory efficiency.
  • CAT, a GPU-accelerated fully homomorphic encryption framework that surpasses existing solutions in functionality and efficiency.
  • PP-SND, a novel Privacy-Preserving Secure Neighbor Discovery protocol that enables devices to perform secure neighbor discovery without revealing their actual identities and locations.

Sources

Privacy Enhanced QKD Networks: Zero Trust Relay Architecture based on Homomorphic Encryption

AES-SpMM: Balancing Accuracy and Speed by Adaptive Edge Sampling Strategy to Accelerate SpMM in GNNs

PilotANN: Memory-Bounded GPU Acceleration for Vector Search

CAT: A GPU-Accelerated FHE Framework with Its Application to High-Precision Private Dataset Query

Privacy-Preserving Secure Neighbor Discovery for Wireless Networks

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