A novel trust-based access control for social networks using fuzzy systems

Vahabli, Sadaf; Ravanmehr, Reza

doi:10.1007/s11280-019-00668-y

Cited by 11 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xu et al 15 proposed a trust‐based access control mechanism Trust2Privacy to protect the privacy of users after releasing information, which can effectively realize the conversion from trust to privacy. Vahabli and Ravanmehr 1 combined with trust‐based access control and fuzzy inference system, by analyzing user requests to determine the user's relationship type, and according to the user's activities in social networks to create attribute matrix, while in the fuzzy inference system to calculate the trust score and specify access permissions. Takalkar and Mahalle 16 realized trust‐based access control according to different roles played by users, and proposed aggregate trust score calculation and security level calculation in multi‐role environment, which solved the access control problem of users playing multiple roles.…”

Section: Related Workmentioning

confidence: 99%

“…In this information age, everyone is equivalent to a walking data packet, and food, clothing, housing, and transportation can generate data. Users on social networks can easily share their personal information and access the personal data of their friends and other users 1 . Unfortunately, some attacks threaten social networks because these networks exchange large amounts of data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A smart access control mechanism based on user preference in online social networks

Shan

2022

Concurrency and Computation

View full text Add to dashboard Cite

SummaryAccess control can effectively protect users' data and privacy in social networks. Different access control models address different data privacy requirements, and the corresponding implementation technologies and performance are also quite different. The existing access control mechanisms of social networks rarely take into account the individual preferences of users, and cannot provide personalized services for users. Therefore, we have studied an analysis mechanism of user preference to quantify user preferences, and then use user preference values and some key information of users' social resources as decision vectors, and proposed an access control mechanism based on user preference (ACMBUP). ACMBUP turns the problem of access decision‐making into a problem of classification, that is, whether to allow access or not to allow access. At the same time, we add an encryption and decryption mechanism in ACMBUP to prevent data leakage and protect data when users interact with access mechanisms. Experiments show that this mechanism can automatically generate appropriate access control policies to meet the potential privacy needs of different users, so as to better protect the privacy of social networks data.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A smart access control mechanism based on user preference in online social networks

Shan

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Privacy and security scholars have developed several approaches to facilitate the definition of access-control policies in OSNs. Particularly, ACPMs seek to automate the generation of ACLs through machine learning [7,10,[28][29][30], formal logic [31,32], and network analysis [14,17,23] among other methods. On a large scale, ACPMs can be classified into community-based [14,17,23] or attribute-based [7,10,29,33], depending on whether they leverage communities or personal attributes for the automatic generation of access-control policies.…”

Section: A Access-control Predictionmentioning

confidence: 99%

Community Detection for Access-Control Decisions: Analysing the Role of Homophily and Information Diffusion in Online Social Networks

Ferreyra,

Hecking,

Aïmeur

et al. 2021

Preprint

View full text Add to dashboard Cite

Access-Control Lists (ACLs) (a.k.a. "friend lists") are one of the most important privacy features of Online Social Networks (OSNs) as they allow users to restrict the audience of their publications. Nevertheless, creating and maintaining custom ACLs can introduce a high cognitive burden on average OSNs users since it normally requires assessing the trustworthiness of a large number of contacts. In principle, community detection algorithms can be leveraged to support the generation of ACLs by mapping a set of examples (i.e. contacts labelled as "untrusted") to the emerging communities inside the user's ego-network. However, unlike users' access-control preferences, traditional community-detection algorithms do not take the homophily characteristics of such communities into account (i.e. attributes shared among members). Consequently, this strategy may lead to inaccurate ACL configurations and privacy breaches under certain homophily scenarios. This work investigates the use of community-detection algorithms for the automatic generation of ACLs in OSNs. Particularly, it analyses the performance of the aforementioned approach under different homophily conditions through a simulation model. Furthermore, since private information may reach the scope of untrusted recipients through the re-sharing affordances of OSNs, information diffusion processes are also modelled and taken explicitly into account. Altogether, the removal of gatekeeper nodes is further explored as a strategy to counteract unwanted data dissemination.

show abstract

“…Research shows that formal control collects focused information around the focused goal of formal control, and social control collects open information around the divergent goal. 30 Therefore, formal control is more conducive to exploitative learning with clear objectives of engineering project team, and social control is more conducive to exploratory learning that engineering project team needs to disseminate information. Based on this, the following assumptions are put forward: H1: The balanced use of formal control and social control is positively correlated with the ambidextrous learning balance of engineering project team. H1a: Compared with social control, formal control has a stronger positive correlation with exploitative learning of engineering project team. H1b: Compared with formal control, social control has a stronger positive correlation with exploratory learning in engineering project team. …”

Section: Theory and Research Hypothesismentioning

confidence: 99%

Ambidextrous learning of engineering project team: Relying on control or BIM AI VR AR MR?

Yang

Cui

2020

International Journal of Engineering Business Management

View full text Add to dashboard Cite

Through formal control and social control, technology platforms can acquire the information knowledge needed for ambidextrous learning of engineering project team, but whether technology platforms influence ambidextrous learning of engineering project team through the intermediary role of ambidextrous control (formal control, social control) is still lack of theoretical and empirical support. In order to explore the influence of the co-use of technology platform and ambidextrous control on the ambidextrous learning of engineering project team, this paper constructs a theoretical framework between the performance of ambidextrous control (formal control, social control) and different types of technology platform (BIM, AI, VR, AR, MR) and different types of learning (exploratory learning, exploitative learning). A questionnaire survey was conducted among 210 project managers. AMOS22.0 and bootstrap software were used for analysis and test, SPSS22.0 was used for hierarchical regression analysis, the results of hierarchical regression analysis show that formal control, social control and the balanced use of both have significant positive correlation with the ambidextrous learning and balanced use of ambidextrous learning of engineering project team. Formal Information Technology Platform (BIM) and Informal Information Technology Platform (AI, VR, AR, MR) have significant positive effects on the ambidextrous learning of engineering project team. The balanced use of formal Information Technology Platform (BIM) and informal Information Technology Platform (AI, AR) has significant positive effects on the ambidextrous learning balance of engineering project team. Formal control and social control mediate the positive correlation between information technology platform (BIM, AI, VR, AR, MR) and ambidextrous learning of engineering project team. In addition, information technology platform (BIM, AI, VR) regulates the impact of formal control and social control on learning (exploratory learning, exploitative learning). Generally speaking, different types of technology platforms and ambidextrous control strategies are still effective for the ambidextrous learning and balance of engineering project team.

show abstract

A novel trust-based access control for social networks using fuzzy systems

Cited by 11 publications

References 29 publications

A smart access control mechanism based on user preference in online social networks

A smart access control mechanism based on user preference in online social networks

Community Detection for Access-Control Decisions: Analysing the Role of Homophily and Information Diffusion in Online Social Networks

Ambidextrous learning of engineering project team: Relying on control or BIM AI VR AR MR?

Contact Info

Product

Resources

About