A Trust based Privacy Providing Model for Online Social Networks

Voloch, Nadav; Gal-Oz, Nurit; Gudes, Ehud

doi:10.1016/j.osnem.2021.100138

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…At the object level, the objects from the shared picture can be detected, such as faces (publisher, co-publisher, unknown), face expressions, body gestures, and other objects (e.g., vehicle registration numbers, personal ornaments, etc.). The overall view object data, such as location, event details, and so on, can be detected at the overall view level [6][7][8][9][10][11][12][13][14][15].…”

Section: A) Visual Detectable Leakagementioning

confidence: 99%

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Privacy-Preserving Photo Sharing on Online Social Networks: A Review

Sajid,

Kavitha

2024

IJSSE

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Online social networking applications and their services have become an essential part of the human lifestyle. These social services help online users in different ways, such as through social visibility, content sharing, communication, promotions, etc. In the same way, the services of online social networks (OSNs) pose privacy risks for their users. The service of photo sharing in OSNs, in particular, causes leakage of online social users' personal information. It's critical to comprehend previous studies that looked at privacypreserving photo sharing and whether users have their privacy protected when sharing photos. The goal of this review is to bridge the gap between the growing demand for image sharing via OSNs and individualized privacy requirements. This effort presents a comprehensive analysis of "privacy-preserving" technologies that specifically address contemporary privacy concerns associated with sharing images on online social networks (OSNs). This study presents a comprehensive analysis framework that focuses on the complete lifecycle of image sharing on online social networks (OSNs). This work presented a review framework called Privacy-Preserving Photo Sharing (PPPS) by categorizing previous works into three stages related to online secure photo sharing. Preprocessing, privacy settings, and photo publishing are the stages used in this survey's design to secure photo sharing. This framework aims to tackle the many privacy challenges and propose appropriate solutions in this multidisciplinary domain. During each phase, we analyse common user behaviours connected to sharing, the privacy concerns that arise from those behaviours, and evaluate representative solutions that are proposed by previous works.

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Section: A) Visual Detectable Leakagementioning

confidence: 99%

“…User credibility is established by factors such as the total number of friends, account age, marital status, gender, etc. that contribute to the user's OSN reputation and level of trustworthiness [8].…”

Section: B) Trust Parametersmentioning

confidence: 99%

Privacy-Preserving Photo Sharing on Online Social Networks: A Review

Sajid,

Kavitha

2024

IJSSE

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“…This section presents a brief overview of our context independent trust-based model [9]. We represent a social network as an undirected graph, where nodes are the OSN users, and edges represent relations between them such as friendship relations.…”

Section: Context Independent Trust-based Modelmentioning

confidence: 99%

“…We provide an infrastructure that uses DRM (Digital Rights Management) methods, to protect the data subject's right over her data, via the data instance publisher, denoted in this paper as the data owner. A digital rights management (DRM) system [9] operates on a computing device when a user requests that a protected piece of digital content be rendered by the computer device in a particular manner. Detecting a digital content journey and its owners or its subjects is challenging in social networks, where the data flow is not controlled.…”

Section: Introductionmentioning

confidence: 99%

A Privacy Providing Context-based Trust Model for OSN and its Relation to GDPR

Voloch,

Gal-Oz,

Gudes

2023

Preprint

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Users’ privacy has become a key objective inthe operation of Online Social Networks (OSN), whichinvolves major concerns regarding personal data exposure and processing. It became a demand with the raiseof legislative initiatives such as the General Data Protection Regulation (GDPR) in the EU that affects mostof the commercial companies, government institutions,and other sectors that process personal data. While thesubject of ensuring privacy in OSN is widely addressedin current research, we focus on challenges related toseveral principles concerning the right to the protectionof personal data, as defined by the GDPR. In previousresearch we have devised a comprehensive Trust-basedModel for Social Networks, that uses Trust, Access Control and Flow Control, to enhance users’ control overtheir data. Users of social networks share data of various contexts, and for a specific user, some contexts aremore sensitive than others. In this paper we use contextdependent trust to build a refined model for controllingpersonal content that aligns with the users’ preferences.We propose a context-based trust model that involvesuser actions, OSN features and OSN activities. It provides a means for revealing the sources of sensitive datadissemination and prevents them from happening. Wevalidate this model by analyzing trust using sentimentanalysis of posts in a real network. The context-basedmodel we present in this paper, addresses the need toestablish an infrastructure for the enforcement of dataprotection rules, concerning users’ privacy in OSN, suchas data minimization and the right-to-be-forgotten. Wealso propose a solution for Digital Rights Management(DRM) to control and manage access to copyrightedmaterial.

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“…These authors conclude that changing the appearance of privacy policies makes online services appear more trustworthy to the user. For its part, Voloch et al (2021), propose a model where user privacy policy is conditioned by three significant aspects, trust, role-based access control and information flow. Due to the relationship shown in different studies between privacy and trust, the second hypothesis to be analyzed is the following:…”

Section: Hypotheses and Research Modelmentioning

confidence: 99%

Evaluation of User Satisfaction and Trust of Review Platforms: Analysis of the Impact of Privacy and E-WOM in the Case of TripAdvisor

2021

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Technological advances have had many advantages like an E-WOM (Electronic Word of Mouth) that has become a very important and powerful tool for users who wish to share their knowledge, experiences and emotions about a product or service. But the use of virtual platforms may affect the privacy of users data. This present study has a twofold objective: first check if the privacy of users when using TripAdvisor, the world’s largest travel platform, has an impact on their satisfaction and trust on the platform. Secondly, the relationship between E-WOM and the variables users trust and satisfaction when using TripAdvisor is examined. In order to achieve the objectives set out, the sample of 390 persons was analyzed. The PLS-SEM method has been used to process the data and test the hypotheses. The results of the analysis have shown that there is a positive and direct relationship between TripAdvisor users’ privacy and satisfaction. The direct and positive relationship between users’ E-WOM and their degree of satisfaction and trust toward the platform has also been confirmed. This study makes a significant contribution to the academic literature on the variables studied, as previous studies presented different results.

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A Trust based Privacy Providing Model for Online Social Networks

Cited by 10 publications

References 20 publications

Privacy-Preserving Photo Sharing on Online Social Networks: A Review

Privacy-Preserving Photo Sharing on Online Social Networks: A Review

A Privacy Providing Context-based Trust Model for OSN and its Relation to GDPR

Evaluation of User Satisfaction and Trust of Review Platforms: Analysis of the Impact of Privacy and E-WOM in the Case of TripAdvisor

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