Titlebook: Security and Privacy in Social Networks and Big Data; 8th International Sy Xiaofeng Chen,Xinyi Huang,Mirosław Kutyłowski Conference proceed

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Colonnade

FedBC: An Efficient and Privacy-Preserving Federated Consensus Schemeeless, semi-trusted cloud platforms can infer the actual data distribution of local users via intermediate characteristics such as gradients. The blockchain proposal has resolved the challenge of consistency in decentralized data sharing. It is difficult to guarantee the accuracy of the block’s data

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choleretic

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CLASP

A Privacy-Preserving Federated Learning with Mutual Verification on Vector Spacess. In this paper, we consider two security issues in the training process of federated learning, i.e., privacy preservation and message verification, which mainly consider the security of the local gradients uploaded by clients and the aggregation result. We give the detail design about the privacy

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