Graph de-anonymization is a technique used to reveal connections between entities in anonymized graphs,which is crucial in detecting malicious activities,network analysis,social network analysis,and more.Despite its p...Graph de-anonymization is a technique used to reveal connections between entities in anonymized graphs,which is crucial in detecting malicious activities,network analysis,social network analysis,and more.Despite its paramount importance,conventional methods often grapple with inefficiencies and challenges tied to obtaining accurate query graph data.This paper introduces a neural-based inexact graph de-anonymization,which comprises an embedding phase,a comparison phase,and a matching procedure.The embedding phase uses a graph convolutional network to generate embedding vectors for both the query and anonymized graphs.The comparison phase uses a neural tensor network to ascertain node resemblances.The matching procedure employs a refined greedy algorithm to discern optimal node pairings.Additionally,we comprehensively evaluate its performance via well-conducted experiments on various real datasets.The results demonstrate the effectiveness of our proposed approach in enhancing the efficiency and performance of graph de-anonymization through the use of graph embedding vectors.展开更多
In cyber-physical systems, multidimensional data fusion is an important method to achieve comprehensive evaluation decisions and reduce data redundancy. In this paper, a data fusion algorithm based on fuzzy set theory...In cyber-physical systems, multidimensional data fusion is an important method to achieve comprehensive evaluation decisions and reduce data redundancy. In this paper, a data fusion algorithm based on fuzzy set theory and Dempster-Shafer(D-S) evidence theory is proposed to overcome the shortcomings of the existing decision-layer multidimensional data fusion algorithms. The basic probability distribution of evidence is determined based on fuzzy set theory and attribute weights, and the data fusion of attribute evidence is combined with the credibility of sensor nodes in a cyber-physical systems network. Experimental analysis shows that the proposed method has obvious advantages in the degree of the differentiation of the results.展开更多
As one of the most promising machine learning frameworks emerging in recent years,Federated learning(FL)has received lots of attention.The main idea of centralized FL is to train a global model by aggregating local mo...As one of the most promising machine learning frameworks emerging in recent years,Federated learning(FL)has received lots of attention.The main idea of centralized FL is to train a global model by aggregating local model parameters and maintain the private data of users locally.However,recent studies have shown that traditional centralized federated learning is vulnerable to various attacks,such as gradient attacks,where a malicious server collects local model gradients and uses them to recover the private data stored on the client.In this paper,we propose a decentralized federated learning against aTtacks(DEFEAT)framework and use it to defend the gradient attack.The decentralized structure adopted by this paper uses a peer-to-peer network to transmit,aggregate,and update local models.In DEFEAT,the participating clients only need to communicate with their single-hop neighbors to learn the global model,in which the model accuracy and communication cost during the training process of DEFEAT are well balanced.Through a series of experiments and detailed case studies on real datasets,we evaluate the excellent model performance of DEFEAT and the privacy preservation capability against gradient attacks.展开更多
基金supported by the National Science Foundation of U.S.(2011845,2315596 and 2244219).
文摘Graph de-anonymization is a technique used to reveal connections between entities in anonymized graphs,which is crucial in detecting malicious activities,network analysis,social network analysis,and more.Despite its paramount importance,conventional methods often grapple with inefficiencies and challenges tied to obtaining accurate query graph data.This paper introduces a neural-based inexact graph de-anonymization,which comprises an embedding phase,a comparison phase,and a matching procedure.The embedding phase uses a graph convolutional network to generate embedding vectors for both the query and anonymized graphs.The comparison phase uses a neural tensor network to ascertain node resemblances.The matching procedure employs a refined greedy algorithm to discern optimal node pairings.Additionally,we comprehensively evaluate its performance via well-conducted experiments on various real datasets.The results demonstrate the effectiveness of our proposed approach in enhancing the efficiency and performance of graph de-anonymization through the use of graph embedding vectors.
基金supported by the National Natural Science Foundation of China (No. 61462089)the Fundamental Research Funds for Beijing University of Civil Engineering and Architecture (No. X18002)
文摘In cyber-physical systems, multidimensional data fusion is an important method to achieve comprehensive evaluation decisions and reduce data redundancy. In this paper, a data fusion algorithm based on fuzzy set theory and Dempster-Shafer(D-S) evidence theory is proposed to overcome the shortcomings of the existing decision-layer multidimensional data fusion algorithms. The basic probability distribution of evidence is determined based on fuzzy set theory and attribute weights, and the data fusion of attribute evidence is combined with the credibility of sensor nodes in a cyber-physical systems network. Experimental analysis shows that the proposed method has obvious advantages in the degree of the differentiation of the results.
基金partially supported by U.S.National Science Foundation(1912753,2011845).
文摘As one of the most promising machine learning frameworks emerging in recent years,Federated learning(FL)has received lots of attention.The main idea of centralized FL is to train a global model by aggregating local model parameters and maintain the private data of users locally.However,recent studies have shown that traditional centralized federated learning is vulnerable to various attacks,such as gradient attacks,where a malicious server collects local model gradients and uses them to recover the private data stored on the client.In this paper,we propose a decentralized federated learning against aTtacks(DEFEAT)framework and use it to defend the gradient attack.The decentralized structure adopted by this paper uses a peer-to-peer network to transmit,aggregate,and update local models.In DEFEAT,the participating clients only need to communicate with their single-hop neighbors to learn the global model,in which the model accuracy and communication cost during the training process of DEFEAT are well balanced.Through a series of experiments and detailed case studies on real datasets,we evaluate the excellent model performance of DEFEAT and the privacy preservation capability against gradient attacks.
