Federated learning offers a partial safeguard for participants’ data privacy. Nevertheless, the current absence of an efficient privacy-preserving federated learning technology tailored for the Internet of Things (IoT) poses a challenge. Numerous privacy-preserving federated learning frameworks have been proposed, primarily relying on homomorphic cryptosystems, yet their suitability for IoT remains limited. Furthermore, the application of federated learning in IoT confronts two significant obstacles: mitigating the substantial communication costs and communication failure rates, and effectively discerning and utilizing high-quality data while discarding low-quality data for collaborative modeling purposes. In order to address these challenges, this paper introduces a privacy-preserving optimal aggregation federated learning framework that relies on the utilization of the multi-key EC-ElGamal cryptosystem (MEEC) and the federated sum optimization algorithm (FSOA), which are characterized by their lightweight nature and fair properties. The proposed MEEC approach aims to tackle the issue of multi-key collaborative computing within the context of federated learning, thereby resulting in reduced communication costs and enhanced communication efficiency. This is achieved through the leverage of the EC-ElGamal cryptosystem, which is known for its ability to generate short keys and ciphertexts. Furthermore, this paper presents a dynamic federated learning framework that incorporates user dynamic quit and join algorithms. The primary objective of this framework is to mitigate the adverse effects of communication failures and enhance power computation on IoT devices. Additionally, an FSOA is devised to ensure the acquisition of optimal training data, thereby preventing the inclusion of low-quality data in the training process. Subsequently, the proposed scheme undergoes rigorous security analysis and performance evaluation. The obtained results unequivocally demonstrate that our scheme outperforms existing solutions in terms of security, practicality, and efficiency with lower communication and computational costs.

Although cloud storage provides convenient data outsourcing services, an untrusted cloud server frequently threatens the integrity and security of the outsourced data. Therefore, it is extremely urgent to design security schemes allowing the users to check the integrity of data with acceptable computational and communication overheads. In this paper, we first propose a public data integrity verification scheme based on the algebraic signature and elliptic curve cryptography. This scheme not only allows the third party authority deputize for users to verify the outsourced data integrity, but also resists malicious attacks such as replay attacks, replacing attack and forgery attacks. Data privacy is guaranteed by symmetric encryption. Furthermore, we construct a novel data structure named divide and conquer hash list, which can efficiently perform data updating operations, such as deletion, insertion, and modification. Compared with the relevant schemes in the literature, security analysis and performance evaluations show that the proposed scheme gains some advantages in integrity verification and dynamic updating.