Federated learning (FL) enables multiple clients to exchange gradients to facilitate collaborative training in edge computing without transferring private data to the cloud server. However, the malicious server may disrupt the model training process by obtaining gradients to infer client information or faking validation results. Considering these threats, we construct a dynamic, secure, and verifiable FL scheme, named DSVFL. Specifically, we propose the fine-grained top-$k$gradient selection algorithm to accelerate model convergence, which can improve model accuracy by up to 49.14%. Furthermore, we propose a single-cloud secure aggregation protocol that guarantees the server cannot access the real model parameters. We also design a trusted verification algorithm to validate the server's aggregation results while resisting collusion between the server and clients. For federation dynamics, we adopt the Shamir's secret sharing in both the aggregation and verification phases, which enables the correct aggregation and decryption of ciphertexts and the independent verification of client signatures, even in cases of client dropouts. Rigorous theoretical analysis demonstrates that DSVFL can protect data privacy and ensure correct training results. Experimental results indicate that DSVFL can reduce clients' computational cost by 48%-63% and communication overhead by 33%-49% compared with existing solutions.

In edge computing environments, the rapid growth of Internet of Things (IoT) devices presents significant challenges for data processing. These devices are often resource-constrained, leading to a tradeoff between achieving efficiency and ensuring security. On one hand, traditional certificateless encryption methods are computationally expensive; on the other hand, offloading the computational load to third-party entities can enhance efficiency but still introduces security risks. To address these issues, this article proposes a novel lightweight certificateless edge-assisted encryption scheme (CL-EAED). Ours scheme offloads computationally intensive tasks to edge servers, ensuring that edge-assisted processing does not expose sensitive information and only needs to be performed once. This approach effectively prevents data leakage and enhances both the efficiency and security of task offloading. Moreover, the CL-EAED scheme achieves IND-CCA security in standard model (SM) and has been validated using the ProVerif tool. Experimental evaluations demonstrate that CL-EAED eliminates the dependency on computationally intensive pairing operations, significantly reducing computational and communication costs. It outperforms existing solutions in terms of energy consumption, latency, and scalability, fully meeting the requirements of practical applications.

Delegation learning is indeed a prevalent approach in privacy-preserving machine learning (PPML), especially when dealing with big data. It specifically involves data owners delegating their data to servers with computational capabilities for training and inference. These servers provide services on a pay-per-use basis. The essence of delegation learning lies in maintaining the integrity of the server’s training while ensuring the privacy of the delegator’s data. However, existing delegation learning schemes struggle to balance security and efficiency, and they cannot guarantee correctness. To tackle these challenges, we propose a cloud-edge collaborative delegation learning framework (CEC-DL) against covert adversaries, which is verifiable and satisfies the guaranteed output delivery (GOD) in security. This is the first time that the covert security assumption is used in a PPML scenario. Furthermore, we design probabilistic verifiable secure addition and subtraction computation protocol (PVS-AaS) and probabilistic verifiable secure multiplication computation protocol (PVS-MUL), which can be used to realize secure addition and multiplication computations in delegation learning without expensive message authentication code (MAC) verification. At the same time, we develop a malicious adversary detection protocol (MADP) that could prevent the malicious actions of potential covert adversaries while ensuring the correct output. Finally, we apply the CEC-DL to the linear regression model to construct privacy preserving linear regression protocol (PP-LRP). Through theoretical analysis and experiments, CEC-DL improves the security, and is more efficient than the verifiable computation of malicious adversaries.