Quantum computing poses a formidable threat to classical cryptographic systems, particularly those safeguarding blockchain-based transactions and user privacy. Conventional algorithms such as RSA and elliptic curve cryptography are vulnerable to quantum attacks through Shor's and Grover's algorithms, jeopardizing the long-term security of digital assets. To address these challenges, this paper proposes a Lightweight Lattice-based Privacy Protection (LLPP) blockchain framework that integrates lattice-based digital signatures with a double encryption mechanism. The framework is designed to provide post-quantum security for transaction authentication while enhancing payment address privacy and reducing direct linkage under public observation, even in the presence of quantum-capable adversaries. A key challenge in deploying lattice-based cryptography on blockchain systems is the substantial storage overhead of signatures and public keys. LLPP addresses this by leveraging the InterPlanetary File System (IPFS) for off-chain storage, recording only 46-byte content identifiers on-chain while maintaining full verifiability. Comprehensive evaluations on the Substrate platform demonstrate that LLPP provides practical post-quantum authentication and privacy protection with signing and verification latencies below 2 milliseconds, making it practical for deployment in quantum-secure blockchain environments.

Packet level measurement in the data plane provides a microscopic view of the network’s state. Although advances in programmable switches and routers make it possible to measure the Sequence of Packet Lengths and Arrival Times (SPLT) in the data plane, collecting this information remains challenging due to limited storage, processing resources, and bandwidth. To address this issue, we propose MES, which Measures and Encodes Simultaneously the packet length and timestamp when each packet passes through the Network Processor (NP) in the switch/router. We design the packet length compression and timestamp compression algorithms to be lightweight and implement the designed algorithms using simple operations supported by the network processor, while taking into account the computation constraints of the NP. Through extensive experiments on five packet traces, we demonstrate that our MES achieves high precision SPLT measurements (up to 99.82% cosine similarity) while reducing storage and bandwidth overhead by up to 87%. Simulations conducted on the BMV2 P4 software switch demonstrate that our designed SPLT measurement mechanism imposes little impact on network throughput and delay.