An Efficient Social Attribute Inference Scheme Based on Social Links and Attribute Relevance

Mao, Jian; Tian, Wenqian; Yugang, Yang; Liu, Jianwei

doi:10.1109/access.2019.2946179

Cited by 14 publications

(14 citation statements)

References 22 publications

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“…Recently, due to availability of the excessive amount of auxiliary information and advanced data mining tools, the scale and the scope of privacy breaches is expanding from an individual identification or SA disclosure to groups identity theft for achieving multiple scientific and business objectives, and identifying communities in SNs having common characteristics or common interests for accurate recommendations. Yin et al [172], Li et al [173], Mao et al [174], Zhang et al [175], [176] 2. user-generated contents in SN measuring similarities of contents in terms of space, time and content dimensions, interest mapping, word embedding technologies on users messages.…”

Section: B De-anonymization Methods Employed By the Adversaries To Jmentioning

confidence: 99%

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

2021

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Section: B De-anonymization Methods Employed By the Adversaries To Jmentioning

confidence: 99%

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

2021

View full text Add to dashboard Cite

“…J Mao et al proposed a relatedness attribute inference method based on relevance attribute inference method (ReAI), which used social relevance attribute network (SRAN) graph-based random walk and restart to find the user's "nearest" attribute node through multi-source data such as the correlation between attributes and social connections [45]. N Z Gong et al combined user attributes, social graph and behaviour to build an enhanced network of social behaviour attribute (SBA), integrated social structure, user behaviour and user attributes into a unified framework [46].…”

Section: Multi Source-based Approachesmentioning

confidence: 99%

Privacy Inference Attack Against Users in Online Social Networks: A Literature Review

Piao

Cui

2021

IEEE Access

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With the rapid development of social networks, users pay more and more attention to the protection of personal information. However, the transmission of users' personal information through social networks will inevitably lead to privacy leakage and make users attacked. A large amount of privacy information can be inferred from the content and social traces published by users, which leads to the rise of privacy inference technology for users in social networks. Social relationship inference and attribute inference are two basic attacks on users' privacy in social networks. This is the first systematic review of privacy inference attacks in social networks. The purpose of this retrospective study is to provide a global summary, summarizing the time trend of the topic, and show the evolution of the topic in the past 15 years. In addition, this paper discusses the trend and development of this topic and finally looks forward to future work. The contribution of our work is helpful for researchers to continue their research in this field.

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“…The cloud data center server and fog access servers only provides data storage, forwarding and synchronization services. 2 They do not interfere with edge computing communications between edge devices. Edge devices can transmit messages with each other directly using unlicensed frequency bands or forward messages by a fog access server for communications without directly reachable.…”

Section: Overview a System Modelmentioning

confidence: 99%

“…A cyber-physical-social system (CPSS) is a multidimensional intelligent system. After mining social relationships behind big data, it can exploit social attributes to control a traditional cyber-physical system [1], [2]. Integrated with the rapidly developing computing-communicationcontrol technologies, CPSSs can realize real-time sensing [3], dynamic control [4], and information services [5], which can be expected to revolutionize our industrial paradigm [6] and improve the quality of human life [7].…”

Section: Introductionmentioning

confidence: 99%

Blockchain Based Dynamic Spectrum Access of Non-Real-Time Data in Cyber-Physical-Social Systems

Fan

Huo

2020

IEEE Access

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Big data sharing in Cyber-Physical-Social Systems (CPSSs) relies on wireless transmission between numerous devices, causing a serious scarcity of radio spectrum resources. Although license-free spectrum access has great potential to alleviate the growing scarcity of spectrum resources, spectrum competition is more intense due to lower access requirements. A blockchain technology may solve this competition problem by introducing a dynamic cycle of ''competition-verification-synchronization-competition". In this paper, we propose a general framework for license-free spectrum resource management in CPSSs based on blockchain technologies and smart contracts. The management framework is mainly used for edge computing of non-real-time data. In particular, we divide spectrum of a local cell into multiple channels and each channel corresponds to a blockchain. Then, we propose a blockchain-KM protocol that may improve transaction processing speed without losing typical attributes of a general blockchain. For the proposed Blockchain-KM protocol, the entire private chain becomes a multi-ring blockchain and users rely on mining or leasing to access wireless spectrum. Different from the traditional mining process, the reward in our mining process is not only virtual currency but also a spectrum access license. Once a miner obtain a spectrum access license, it will exploit the license to transmit its messages over wireless links. Also, the miner may sell its license by an auction when it does not want to transmit messages. In the auction, we introduce a virtual currency, called as Xcoin, for spectrums or other trading (e.g., paid edge computing services).INDEX TERMS Cyber-physical-social systems, edge computing, spectrum access, blockchain, smart contract.

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An Efficient Social Attribute Inference Scheme Based on Social Links and Attribute Relevance

Cited by 14 publications

References 22 publications

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Privacy Inference Attack Against Users in Online Social Networks: A Literature Review

Blockchain Based Dynamic Spectrum Access of Non-Real-Time Data in Cyber-Physical-Social Systems

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