The data in Mobile Edge Computing(MEC)contains tremendousmarket value,and data sharing canmaximize the usefulness of the data.However,certain data is quite sensitive,and sharing it directly may violate privacy.Vertica...The data in Mobile Edge Computing(MEC)contains tremendousmarket value,and data sharing canmaximize the usefulness of the data.However,certain data is quite sensitive,and sharing it directly may violate privacy.Vertical Federated Learning(VFL)is a secure distributed machine learning framework that completes joint model training by passing encryptedmodel parameters rather than raw data,so there is no data privacy leakage during the training process.Therefore,the VFL can build a bridge between data demander and owner to realize data sharing while protecting data privacy.Typically,the VFL requires a third party for key distribution and decryption of training results.In this article,we employ the consortium blockchain instead of the traditional third party and design a VFL architecture based on the consortium blockchain for data sharing in MEC.More specifically,we propose a V-Raft consensus algorithm based on Verifiable Random Functions(VRFs),which is a variant of the Raft.The VRaft is able to elect leader quickly and stably to assist data demander and owner to complete data sharing by VFL.Moreover,we apply secret sharing todistribute the private key to avoid the situationwhere the training result cannot be decrypted if the leader crashes.Finally,we analyzed the performance of the V-Raft and carried out simulation experiments,and the results show that compared with Raft,the V-Raft has higher efficiency and better scalability.展开更多
With the development of cloud computing technology,more and more data owners upload their local data to the public cloud server for storage and calculation.While this can save customers’operating costs,it also poses ...With the development of cloud computing technology,more and more data owners upload their local data to the public cloud server for storage and calculation.While this can save customers’operating costs,it also poses privacy and security challenges.Such challenges can be solved using secure multi-party computation(SMPC),but this still exposes more security issues.In cloud computing using SMPC,clients need to process their data and submit the processed data to the cloud server,which then performs the calculation and returns the results to each client.Each client and server must be honest.If there is cooperation or dishonest behavior between clients,some clients may profit from it or even disclose the private data of other clients.This paper proposes the SMPC based on a Partially-Homomorphic Encryption(PHE)scheme in which an addition homomorphic encryption algorithm with a lower computational cost is used to ensure data comparability and Zero-Knowledge Proof(ZKP)is used to limit the client’s malicious behavior.In addition,the introduction of Oblivious Transfer(OT)technology also ensures that the semi-honest cloud server knows nothing about private data,so that the cloud server of this scheme can calculate the correct data in the case of malicious participant models and safely return the calculation results to each client.Finally,the security analysis shows that the scheme not only ensures the privacy of participants,but also ensures the fairness of the comparison protocol data.展开更多
基金funded by the National Natural Science Foundation(61962009)the National Natural Science Foundation(62202118)+1 种基金Top Technology Talent Project from Guizhou Education Department(Qianjiao ji[2022]073)Foundation of Guangxi Key Laboratory of Cryptography and Information Security(GCIS202118).
文摘The data in Mobile Edge Computing(MEC)contains tremendousmarket value,and data sharing canmaximize the usefulness of the data.However,certain data is quite sensitive,and sharing it directly may violate privacy.Vertical Federated Learning(VFL)is a secure distributed machine learning framework that completes joint model training by passing encryptedmodel parameters rather than raw data,so there is no data privacy leakage during the training process.Therefore,the VFL can build a bridge between data demander and owner to realize data sharing while protecting data privacy.Typically,the VFL requires a third party for key distribution and decryption of training results.In this article,we employ the consortium blockchain instead of the traditional third party and design a VFL architecture based on the consortium blockchain for data sharing in MEC.More specifically,we propose a V-Raft consensus algorithm based on Verifiable Random Functions(VRFs),which is a variant of the Raft.The VRaft is able to elect leader quickly and stably to assist data demander and owner to complete data sharing by VFL.Moreover,we apply secret sharing todistribute the private key to avoid the situationwhere the training result cannot be decrypted if the leader crashes.Finally,we analyzed the performance of the V-Raft and carried out simulation experiments,and the results show that compared with Raft,the V-Raft has higher efficiency and better scalability.
基金supported by the National Natural Science Foundation of China under Grant No.(62202118.61962009)And in part by Natural Science Foundation of Shandong Province(ZR2021MF086)+1 种基金And in part by Top Technology Talent Project from Guizhou Education Department(Qian jiao ji[2022]073)And in part by Foundation of Guangxi Key Laboratory of Cryptography and Information Security(GCIS202118).
文摘With the development of cloud computing technology,more and more data owners upload their local data to the public cloud server for storage and calculation.While this can save customers’operating costs,it also poses privacy and security challenges.Such challenges can be solved using secure multi-party computation(SMPC),but this still exposes more security issues.In cloud computing using SMPC,clients need to process their data and submit the processed data to the cloud server,which then performs the calculation and returns the results to each client.Each client and server must be honest.If there is cooperation or dishonest behavior between clients,some clients may profit from it or even disclose the private data of other clients.This paper proposes the SMPC based on a Partially-Homomorphic Encryption(PHE)scheme in which an addition homomorphic encryption algorithm with a lower computational cost is used to ensure data comparability and Zero-Knowledge Proof(ZKP)is used to limit the client’s malicious behavior.In addition,the introduction of Oblivious Transfer(OT)technology also ensures that the semi-honest cloud server knows nothing about private data,so that the cloud server of this scheme can calculate the correct data in the case of malicious participant models and safely return the calculation results to each client.Finally,the security analysis shows that the scheme not only ensures the privacy of participants,but also ensures the fairness of the comparison protocol data.
