KEYWORDS: Education and training, Data modeling, Deep learning, Machine learning, Computer simulations, Data privacy, Systems modeling, Computer security
Federated learning enables multiple parties to jointly train a global model without sharing the original data, which has attracted much attention. Existing research work shows that even sharing local gradients will leak local data. What's worse, the server may deliberately tamper with the aggregation results, resulting in user privacy leakage or other attacks, so users need to verify the correctness of the calculation results returned by the server. In this paper, we design a verifiable privacy-preserving scheme where the server is honest and curious but has the additional ability to forge the aggregated results. The proposed scheme can guarantee the privacy gradient of honest users under the condition that no more than t users collude with the server. During the execution of the protocol, the user is allowed to drop out at any phase, and the aggregated results is kept secret from the server. In addition, each user can verify the correctness of the server’s calculation results, which is the ciphertext of the aggregated results.
KEYWORDS: Clouds, Matrices, Computer security, Internet of things, Receivers, Mobile devices, Cloud computing, Telecommunications, Systems modeling, Internet
Due to the storage and computing ability of cloud technology, many protocols are suitable for deployment on cloud servers. Private set intersection (PSI) is practical technology in data mining, similar document detection and so on. In some cloud-based IoT system and mobile devices have poor ability to calculate the intersection. In this scenario, we design a protocol make the part of the computing intersection execute on the cloud server, called Efficient-DPSI, which supports flexible parameters. Experimental results show that our Efficient-DPSI protocol is more efficient with existing related delegated PSI protocols.
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