Xiuhua Lu

The rapidly evolving Internet of Things (IoT) continues to serve as a critical bridge between the physical world and digital space, driving increasing demands for optimized communication capabilities and time-sensitive data acquisition. However, traditional cloud computing architectures are becoming increasingly insufficient to meet these stringent demands. Mobile Edge Computing (MEC) has thus emerged as a promising paradigm. By delegating data processing tasks from centralized cloud servers to edge nodes (ENs) located near end-users, MEC significantly enhances service efficiency while simultaneously introducing heightened privacy and security risks. To mitigate both external threats during task interactions and internal adversaries within the network, this paper proposes a Lightweight Verification and Protection (LVP) strategy for ENs. LVP primarily leverages secure computation techniques to ensure the anonymity and confidentiality of private information during transmission and verification. It further incorporates a reputation-based evaluation framework to mitigate the impact of insider threats. Specifically, the integrity of user-uploaded task data is first verified, followed by anonymous matching between users and ENs through paired index. A reputation-based anonymous authentication algorithm is then designed to prevent exposure or linkage of reputation information during usage. Finally, reputation values are dynamically updated by jointly considering temporal relevance and historical behavior. Theoretical analysis confirms the robustness and correctness of all LVP sub-algorithms, while experimental evaluations demonstrate the scheme’s effectiveness and feasibility under low computational and communication overhead.

Secure healthcare information exchange (HIE) is critical to improving medical services, enabling data interoperability, and ensuring patient privacy. However, the increasing threat posed by quantum computing challenges the reliability of conventional cryptographic mechanisms. To address this, we propose a post-quantum secure healthcare data-sharing scheme that combines the Extended Merkle Signature Scheme (XMSS) and consortium blockchain technology to guarantee the integrity, authenticity, and traceability of electronic medical records (EMRs). Furthermore, the scheme incorporates autonomous artificial intelligence (AI) to assist healthcare professionals in generating accurate and intelligent diagnostic reports, enhancing clinical decision-making. We theoretically analyze the scheme's security in the random oracle model, demonstrating that it effectively resists various threats. Performance evaluation shows that the scheme is particularly suitable for HIE scenarios as it reduces about 49% in total computational overheads and 36% in blockchain storage compared to other schemes

Blockchain is renowned for its immutability, a feature that ensures recorded data cannot be modified or deleted. However, malicious entities can exploit this immutability to permanently embed objectionable data. Moreover, the immutability of blockchain can conflict with the “right to be forgotten” provision in the privacy protection laws of the GDPR. Therefore, a seminal redactable blockchain solution is proposed to address the above problem. Recently, one of the primary focuses in redactable blockchain solutions is the design of global editing permission control. The design leverages the consensus voting mechanism, with its core objective being to prevent excessive centralization of editing power, thereby preserving the decentralized nature of blockchain technology. Meanwhile, such schemes exhibit the following phenomena: (i) the members of the editorial decision-making group are fixed and unchanging, and (ii) blockchain nodes display lazy voting behaviors during the editorial voting process. Considering these factors, we introduce a Publicly Redactable Blockchain scheme with an off-chain reputation-based Consensus Protocol (PRBCP). In this scheme, any node in the blockchain network has the opportunity to become an editorial node and perform editing operations. We design a reputation-based off-chain editorial voting consensus protocol leveraging the threshold signature scheme, which enables dynamic updates to the editorial decision-making group membership and enhances nodes’ participation in editorial voting. In addition, we conduct rigorous security proofs and experimental efficiency analyses for our scheme. The results demonstrate that the PRBCP is both secure and efficient. Finally, we instantiate our scheme as a redactable medical blockchain (RMB) system for storing electronic medical records (EMRs).

The Internet of Things (IoT) serves as a crucial tool supporting smart lifestyles. As a significant application of IoT, the speed and security of transaction processing in a global cold chain logistics system have garnered considerable attention. Addressing these two issues, this paper introduces the self-management chameleon hash with double-auditability and revocability (SCHDR). Our redactable blockchain scheme is based on this chameleon hash function. We implement a self-management mechanism with a random value switching technique based on bilinear pairing and combine dual long-term trapdoor keys and label-binding technologies to achieve a double-auditability mechanism. We also effectively correct malicious rewriting behaviors and revoke the permissions for malicious modifications. The security of our scheme is based on the Bilinear Diffie-Hellman (BDH) assumption, and experiments demonstrate that our scheme possesses high efficiency. Furthermore, our proposal can also be applicable to other IoT applications with similar security requirements.

Federated learning offers a paradigm to the challenge of preserving privacy in distributed machine learning. However, datasets distributed across each client in the real world are inevitably heterogeneous, and if the datasets can be globally aggregated, they tend to be long-tailed distributed, which greatly affects the performance of the model. The traditional approach to federated learning primarily addresses the heterogeneity of data among clients, yet it fails to address the phenomenon of class-wise bias in global long-tailed data. This results in the trained model focusing on the head classes while neglecting the equally important tail classes. Consequently, it is essential to develop a methodology that considers classes holistically. To address the above problems, we propose a new method FedLF, which introduces three modifications in the local training phase: adaptive logit adjustment, continuous class centred optimization, and feature decorrelation. We compare seven state-of-the-art methods with varying degrees of data heterogeneity and long-tailed distribution. Extensive experiments on benchmark datasets CIFAR-10-LT and CIFAR-100-LT demonstrate that our approach effectively mitigates the problem of model performance degradation due to data heterogeneity and long-tailed distribution. our code is available at https://github.com/18sym/FedLF.

Based on the NTRU trapdoor used in NIST’s Falcon, a signcryption scheme following the sign-then-encrypt paradigm is constructed. The existing partitioning technique based on Waters hash over the lattice can not complete the security reduction in the standard model for the signature part due to the “partiality” of the pre-image generated with the NTRU trapdoor. To address this, a variant of Waters hash over small integers is proposed and, the probability of the successful reduction is analyzed. The resulting signcryption achieves existential unforgeability under the adaptive chosen-message attacks. By utilizing the uniqueness of the secret and the noise in an NTRU instance, the tag used in encryption is eliminated. Furthermore, a method to construct tamper-sensitive lattice public key encryption is proposed. This approach implants the ciphertext-sensitive information into the lattice public key encryption and binds it to the encrypted information. The malleability to the public key ciphertext triggers the change of the message–signature pair so that the IND-CCA2 security of the entire ciphertext can be guaranteed by the signature for the message. Thanks to the rational design and the efficiency of the NTRU trapdoor, the computational overhead of the proposed scheme is reduced significantly compared to the existing lattice-based signcryption scheme, reaching orders of magnitude improvement in efficiency. The experiment shows that the proposed scheme is efficient.

Before the transaction data in the blockchain is successfully linked, its signature must be publicly verified by a large number of nodes in the blockchain, which is also one of the ways to leak transaction information. To alleviate the contradiction between the public verifiability of signatures and the protection of transaction privacy, we introduce a verifiably encrypted signature scheme into the blockchain. A verifiably encrypted signature scheme contains two parts of signature information: the encrypted signature is used for public verification, and the ordinary signature is used for internal verification. To reach this goal even better, we design a new lattice-based verifiably encrypted signature scheme, which separates the parameter settings of the signer and the adjudicator, and replaces the Gaussian sampling algorithm with a small range of uniform random sampling, achieving better efficiency and security.