User Behavior in Social Networks Toward Privacy and Trust: Literature Review

Almogbel, Razan Saleh; Alkhalifah, Ali

doi:10.3991/ijim.v16i01.27763

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…And when K is too large, there will be additional computational cost and more noise may be introduced. We implemented our proposed method using Python-3.7 4 and Pytorch-1.6 5 . All the experiments were conducted on the same machine with Linux system (Ubuntu 5.4.0), Intel(R) Xeon(R) CPU E5-2680, 128GB RAM and 2 NVIDIA 1080Ti GPUs.…”

Section: ) Parameter Settingsmentioning

confidence: 99%

TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature

Huo¹,

Jin²,

Liang³

et al. 2022

Preprint

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Trust evaluation is critical for many applications such as cyber security, social communication and recommender systems. Users and trust relationships among them can be seen as a graph. Graph neural networks (GNNs) show their powerful ability for analyzing graph-structural data. Very recently, existing work attempted to introduce the attributes and asymmetry of edges into GNNs for trust evaluation, while failed to capture some essential properties (e.g., the propagative and composable nature) of trust graphs. In this work, we propose a new GNN based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation. Specifically, TrustGNN designs specific propagative patterns for different propagative processes of trust, and distinguishes the contribution of different propagative processes to create new trust. Thus, TrustGNN can learn comprehensive node embeddings and predict trust relationships based on these embeddings. Experiments on some widely-used real-world datasets indicate that TrustGNN significantly outperforms the state-of-the-art methods. We further perform analytical experiments to demonstrate the effectiveness of the key designs in TrustGNN.

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Section: ) Parameter Settingsmentioning

confidence: 99%

TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature

Huo¹,

Jin²,

Liang³

et al. 2022

Preprint

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show abstract

“…Theme 2-data privacy. Under the technological challenge, data privacy [20]- [21] is one of the biggest concerns for telecommunication providers that adopt big data. The the challenge requires the telecommunication provider to explore the techniques and mechanisms to protect the privacy of the data while catering to the business needs and profit.…”

Section: Technological Challengesmentioning

confidence: 99%

Security and Privacy Challenges of Big Data Adoption: A Qualitative Study in Telecommunication Industry

Anawar

Othman²,

Selamat³

et al. 2022

Int. J. Interact. Mob. Technol.

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The telecommunication industry is the leading industry in big data trends as this industry has the most capable infrastructure for big data. However, the adoption of big data in telecommunication services also raises important security and privacy challenges due to the high volume, velocity, and variety of big data characteristics. To address the issue, this study shed light on the security and privacy challenges of big data adoption in the telecommunication industry. This study focuses on investigating the security and privacy challenges for data users in telecommunication services from the lens of the TOE framework and examines the mitigation strategies to address the privacy and security challenges. This study is conducted using qualitative methodology. From the perspectives of data users (telecommunication providers), it could be concluded that data management, data privacy, data compliance, and regulatory orchestration challenges are the most pressing concerns in big data adoption. This study offers contributions in presenting a thematic classification of security and privacy challenges and their mitigation strategies for big data adoption in the telecommunication industry. The thematic classification highlights potential gaps for future research in the big data security domain. This study is significant in that it provides empirical evidence for the perspectives of telecommunication data users in addressing privacy and security issues that are related to big data adoption.

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“…In Figure 2, five new edges have been introduced to change the structure of G for privacy preservation. Recently, many solutions have been proposed to preserve the privacy of SN users in G publishing [15][16][17][18][19][20][21]. These solutions have been used to preserve either nodes' or edges' privacy in the release of G. Recently, differential privacy-based solutions have also been proposed to alter the G's structure for privacy preservation [22].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks

2022

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Owning to the massive growth in internet connectivity, smartphone technology, and digital tools, the use of various online social networks (OSNs) has significantly increased. On the one hand, the use of OSNs enables people to share their experiences and information. On the other hand, this ever-growing use of OSNs enables adversaries to launch various privacy attacks to compromise users’ accounts as well as to steal other sensitive information via statistical matching. In general, a privacy attack is carried out by the exercise of linking personal data available on the OSN site and social graphs (or statistics) published by the OSN service providers. The problem of securing user personal information for mitigating privacy attacks in OSNs environments is a challenging research problem. Recently, many privacy-preserving solutions have been proposed to secure users’ data available over OSNs from prying eyes. However, a systematic overview of the research dynamics of OSN privacy, and findings of the latest privacy-preserving approaches from a broader perspective, remain unexplored in the current literature. Furthermore, the significance of artificial intelligence (AI) techniques in the OSN privacy area has not been highlighted by previous research. To cover this gap, we present a comprehensive analysis of the state-of-the-art solutions that have been proposed to address privacy issues in OSNs. Specifically, we classify the existing privacy-preserving solutions into two main categories: privacy-preserving graph publishing (PPGP) and privacy preservation in application-specific scenarios of the OSNs. Then, we introduce a high-level taxonomy that encompasses common as well as AI-based privacy-preserving approaches that have proposed ways to combat the privacy issues in PPGP. In line with these works, we discuss many state-of-the-art privacy-preserving solutions that have been proposed for application-specific scenarios (e.g., information diffusion, community clustering, influence analysis, friend recommendation, etc.) of OSNs. In addition, we discuss the various latest de-anonymization methods (common and AI-based) that have been developed to infer either identity or sensitive information of OSN users from the published graph. Finally, some challenges of preserving the privacy of OSNs (i.e., social graph data) from malevolent adversaries are presented, and promising avenues for future research are suggested.

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User Behavior in Social Networks Toward Privacy and Trust: Literature Review

Cited by 7 publications

References 23 publications

TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature

TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature

Security and Privacy Challenges of Big Data Adoption: A Qualitative Study in Telecommunication Industry

A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks

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