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Tsinghua Science and Technology 2024年第3期29卷 897-910页

作者： Wenmin Lin Min Zhu Xinyi Zhou Ruowei Zhang Xiaoran Zhao Shigen Shen Lu Sun Alibaba Business School Hangzhou Normal UniversityHangzhou 311121China blockchain laboratory of agricultural vegetables Weifang University of Science and TechnologyShouguang 262700China School of Computer Science Qufu Normal UniversityRizhao 276827China School of Information Engineering Huzhou UniversityHuzhou 313000China

Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices.However,the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation methods.In view of these challenges,we propose a deep neural collaborative filtering based service recommendation method with multi-source data(i.e.,NCF-MS)in this paper,which adopts the cloud-edge collaboration computing paradigm to build recommendation model.More specifically,the Stacked Denoising Auto Encoder(SDAE)module is adopted to extract user/service features from auxiliary user profiles and service attributes.The Multiple Layer Perceptron(MLP)module is adopted to integrate the auxiliary user/service features to train the recommendation model.Finally,we evaluate the effectiveness of the NCF-MS method on three public datasets.The experimental results show that our proposed method achieves better performance than existing methods.

关键词： deep neural collaborative filtering multi-source data cloud-edge collaboration application stackeddenoising auto encoder multiple layer perceptron

FedCDR:Privacy-preserving federated cross-domain recommendation

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Digital Communications and Networks 2022年第4期8卷 552-560页

作者： Dengcheng Yan Yuchuan Zhao Zhongxiu Yang Ying Jin Yiwen Zhang School of Computer Science and Technology Anhui UniversityHefeiChina weifang key laboratory of blockchain on agricultural vegetables Weifang University of Science and TechnologyWeifangChina Department of Management Hefei UniversityHefeiChina

Cross-Domain Recommendation(CDR)aims to solve data sparsity and cold-start problems by utilizing a relatively information-rich source domain to improve the recommendation performance of the data-sparse target domain.However,most existing approaches rely on the assumption of centralized storage of user data,which undoubtedly poses a significant risk of user privacy leakage because user data are highly privacy-sensitive.To this end,we propose a privacy-preserving Federated framework for Cross-Domain Recommendation,called FedCDR.In our method,to avoid leakage of user privacy,a general recommendation model is trained on each user's personal device to obtain embeddings of users and items,and each client uploads weights to the central server.The central server then aggregates the weights and distributes them to each client for updating.Furthermore,because the weights implicitly contain private information about the user,local differential privacy is adopted for the gradients before uploading them to the server for better protection of user privacy.To distill the relationship of user embedding between two domains,an embedding transformation mechanism is used on the server side to learn the cross-domain embedding transformation model.Extensive experiments on real-world datasets demonstrate that ourmethod achieves performance comparable with that of existing data-centralized methods and effectively protects user privacy.

关键词： Cross-domain recommendation Federated learning Privacy preserving

A Truncated SVD-Based ARIMA Model for Multiple QoS Prediction in Mobile Edge Computing

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Tsinghua Science and Technology 2022年第2期27卷 315-324页

作者： Chao Yan Yankun Zhang Weiyi Zhong Can Zhang Baogui Xin the College of Economic and Management Shandong University of Science and TechnologyQingdao 266590China weifang key laboratory of blockchain on agricultural vegetables Weifang University of Science and TechnologyWeifang 262700China the School of Computer Science Qufu Normal UniversityRizhao 276826China

In the mobile edge computing environments,Quality of Service(QoS)prediction plays a crucial role in web service recommendation.Because of distinct features of mobile edge computing,i.e.,the mobility of users and incomplete historical QoS data,traditional QoS prediction approaches may obtain less accurate results in the mobile edge computing environments.In this paper,we treat the historical QoS values at different time slots as a temporal sequence of QoS matrices.By incorporating the compressed matrices extracted from QoS matrices through truncated Singular Value Decomposition(SVD)with the classical ARIMA model,we extend the ARIMA model to predict multiple QoS values simultaneously and efficiently.Experimental results show that our proposed approach outperforms the other state-of-the-art approaches in accuracy and efficiency.

关键词： edge computing QoS prediction Auto Regressive Integrated Moving Average(ARIMA) truncated Singular Value Decomposition(SVD)

Vertical Federated Learning Based on Consortium blockchain for Data Sharing in Mobile Edge Computing

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 345-361页

作者： Yonghao Zhang Yongtang Wu Tao Li Hui Zhou Yuling Chen State key laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China blockchain laboratory of agricultural vegetables Weifang University of Science and TechnologyWeifang262700China Guangxi key laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin541004China

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.

关键词： Mobile edge computing vertical federated learning consortium blockchain consensus algorithm

An Effective Security Comparison Protocol in Cloud Computing

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Computers, Materials & Continua 2023年第6期75卷 5141-5158页

作者： Yuling Chen Junhong Tao Tao Li Jiangyuan Cai Xiaojun Ren State key laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550000China Guangxi key laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin550000China POWERCHINA Guizhou Electric Power Engineering Co. Ltd.Guiyang550000China blockchain laboratory of agricultural vegetables Weifang University of Science and TechnologyWeifang261000China

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.

关键词： Secure comparison protocols zero-knowledge proof homomorphic encryption cloud computing

A label noise filtering and label missing supplement framework based on game theory

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数字通信与网络:英文版 2023年第4期9卷 887-895页

作者： Yuwen Liu Rongju Yao Song Jia Fan Wang Ruili Wang Rui Ma Lianyong Qi College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266580China weifang key laboratory of blockchain on agricultural vegetables Weifang University of Science and TechnologyShouguangChina China Unicom Taian Branch China School of Natural and Computational Sciences Massey UniversityNew Zealand General Education Department Shandong First Medical UniversityShandong Academy of Medical SciencesTai'an271000China College of Computer Science and Technology Zhejiang UniversityHangzhouChina

Labeled data is widely used in various classification tasks.However,there is a huge challenge that labels are often added artificially.Wrong labels added by malicious users will affect the training effect of the model.The unreliability of labeled data has hindered the research.In order to solve the above problems,we propose a framework of Label Noise Filtering and Missing Label Supplement(LNFS).And we take location labels in Location-Based Social Networks(LBSN)as an example to implement our framework.For the problem of label noise filtering,we first use FastText to transform the restaurant's labels into vectors,and then based on the assumption that the label most similar to all other labels in the location is most representative.We use cosine similarity to judge and select the most representative label.For the problem of label missing,we use simple common word similarity to judge the similarity of users'comments,and then use the label of the similar restaurant to supplement the missing labels.To optimize the performance of the model,we introduce game theory into our model to simulate the game between the malicious users and the model to improve the reliability of the model.Finally,a case study is given to illustrate the effectiveness and reliability of LNFS.

关键词： Label noise FastText Cosine similarity Game theory LSTM