Privacy-Preserving Content Dissemination for Vehicular Social Networks: Challenges and Solutions

Wang, Xiaojie; Ning, Zhaolong; Zhou, MengChu; Hu, Xiping; Wang, Lei; Zhang, Yan; Yu, F. Richard; Hu, Bin

doi:10.1109/comst.2018.2882064

Cited by 130 publications

(57 citation statements)

References 187 publications

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“…In the future, we will focus on dynamic motif clustering for real-time network management (Ning et al, 2018;Ning, Huang & Wang, 2019;Wang et al, 2018a). In addition, network security (Wang et al, 2018b and crowdsourcing based methods (Ning et al, 2019a(Ning et al, , 2019b) also deserve to be investigated.…”

Section: Resultsmentioning

confidence: 99%

COMICS: a community property-based triangle motif clustering scheme

Feng

Zhang

et al. 2019

PeerJ Computer Science

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With the development of science and technology, network scales of various fields have experienced an amazing growth. Networks in the fields of biology, economics and society contain rich hidden information of human beings in the form of connectivity structures. Network analysis is generally modeled as network partition and community detection problems. In this paper, we construct a community property-based triangle motif clustering scheme (COMICS) containing a series of high efficient graph partition procedures and triangle motif-based clustering techniques. In COMICS, four network cutting conditions are considered based on the network connectivity. We first divide the large-scale networks into many dense subgraphs under the cutting conditions before leveraging triangle motifs to refine and specify the partition results. To demonstrate the superiority of our method, we implement the experiments on three large-scale networks, including two co-authorship networks (the American Physical Society (APS) and the Microsoft Academic Graph (MAG)), and two social networks (Facebook and gemsec-Deezer networks). We then use two clustering metrics, compactness and separation, to illustrate the accuracy and runtime of clustering results. A case study is further carried out on APS and MAG data sets, in which we construct a connection between network structures and statistical data with triangle motifs. Results show that our method outperforms others in both runtime and accuracy, and the triangle motif structures can bridge network structures and statistical data in the academic collaboration area. massive scale related data, such as digital libraries and scholarly networks . As collaboration behaviors among scholars are becoming frequent, collaboration networks are generally in large-scale and contain rich collaboration information, reflecting the cooperation patterns among scholars in different research areas. Bordons et al. (1996) regard the academic teams as scientists communities, in which scholars can share research methods, materials, and financial resources rather than institutions organized by fixed structures (Barjak & Robinson, 2008). Furthermore, the ternary closures in social networks constitute a minimal stable structure; that is, a loop with three nodes. The number of ternary closures in social networks changes over time, which reveals the evolvement of human social behaviors. Besides, the definition of a clustering coefficient is based on the distributions of ternary closures. Milo et al. (2002) defined small network structures as motifs to present interconnections in complex networks by numbers that are significantly higher than those in randomized networks. Motifs can define universal classes of networks, and researchers are carrying on the motif detection experiments on networks from different areas, such as biochemistry, neurobiology, and engineering, to uncover the existence of motifs and the corresponding structure information in networks (Ribeiro, Silva & Kaiser, 2009;Bian & Zhang, 2016). Hence, triangle mo...

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Section: Resultsmentioning

confidence: 99%

COMICS: a community property-based triangle motif clustering scheme

Feng

Zhang

et al. 2019

PeerJ Computer Science

Self Cite

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“…Recently, much of growing interest has been pursued in the context of data storage security on cloud services 25‐33 . These techniques, which can be useful to ensure the data security and the privacy in cloud computing, are all focusing on using cryptographic and access‐based control approaches.…”

Section: Background and Related Workmentioning

confidence: 99%

Password‐based encryption approach for securing sensitive data

Mustaçoğlu

Çatak

Fox

2020

Security and Privacy

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The direction of computing is affected and lead by several trends. First, we have the Data Overwhelm from Commercial sources (eg, Amazon), Community sources (eg, Twitter), and Scientific applications (eg, Genomics). Next, we have several light‐weight clients belong to many devices spanning from smartphones, tablets to sensors. Then, clouds are getting popular due to their advantages since they are cheaper, greener, and easy to use compared to traditional systems. Finally, sensitive data stored as a plain text on a cloud system would be vulnerable to unauthorized access and the security become an important aspect. We believe that these advancements steer both research and education, and will put together as we look at data security in the cloud. We introduce a password‐based encryption (PBE) approach to protect sensitive data, investigate the performance metrics of the proposed approach, and present the experimental results for the key generation and the encryption/decryption calculations.

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“…Le‐Khac et al discusses the recent challenges and case studies related to smart vehicle forensics. The lack of total network states, and high mobility of smart vehicles makes it more vulnerable to the potential attacks when content dissemination is achieved via vehicular social networks . The identification and examination of the artifacts related to different attack scenario is still a challenging area of research.…”

Section: Background and Related Workmentioning

confidence: 99%

An intelligent approach for examining and detecting target data fragments in suspected large storage drives

Bharadwaj

Singh

2019

Security and Privacy

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The ever increasing capacity of storage devices is becoming a formidable obstruction to the digital forensic community due to its substantial investigation time and preservation space requirements. However, the investigation process becomes more complicated, whenever the fragmented or partially overwritten drives need forensic consideration. It is becoming extremely necessary to unfold novel methods the examination of storage drives involving a bulk volume of data. In this paper, the differential evolution (DE) technique is utilized with an unsupervised mean-shift clustering approach in the field of digital forensics for intelligently locating the traces of target file in suspected storage drives or raw images. The proposed methodology leverages the drives' geometrical information in DE for obtaining random sector samples followed by fitness value and sector hash computation. The clustering algorithm evaluates the obtained intelligence and assists in estimating the regions of forensic significance in the drive, instead of considering the entire drive contents. As an outcome, a proof-of-concept Python based command line tool has been developed and released to support the study and further enhancement. The experiments and case studies demonstrated using the drives of different capacities demonstrates the efficacy of the proposed method. K E Y W O R D Sdifferential evolution, digital forensics, huge data volumes, mean shift clustering, storage drive, target file 1 Security Privacy. 2019;2:e71.wileyonlinelibrary.com/journal/spy2

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Privacy-Preserving Content Dissemination for Vehicular Social Networks: Challenges and Solutions

Cited by 130 publications

References 187 publications

COMICS: a community property-based triangle motif clustering scheme

COMICS: a community property-based triangle motif clustering scheme

Password‐based encryption approach for securing sensitive data

An intelligent approach for examining and detecting target data fragments in suspected large storage drives

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