A Trust based Privacy Providing Model for Online Social Networks

Q1 Social Sciences Online Social Networks and Media Pub Date : 2021-07-01 DOI:10.1016/j.osnem.2021.100138
Nadav Voloch , Nurit Gal-Oz , Ehud Gudes
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引用次数: 7

Abstract

Online Social Networks (OSN) have become a central means of communication and interaction between people around the world. The essence of privacy has been challenged through the past two decades as technological advances enabled benefits and social visibility to active members that share content in online communities. While OSN users share personal content with friends and colleagues, they are not always fully aware of the potential unintentional exposure of their information to various people including adversaries, social bots, fake users, spammers, or data-harvesters. Preventing this information leakage is a key objective of many security models developed for OSNs including Access Control, Relationship based models, Trust based models and Information Flow control. Following previous research, we assert that a combined approach is required to overcome the shortcoming of each model. In this paper we present a new model to protect users' privacy that is composed of three main phases addressing three of its major aspects: trust, role-based access control and information flow. This model considers a user's sub-network and classifies the user's direct connections to roles. It relies on public information such as total number of friends, age of user account, and friendship duration to characterize the quality of the network connections. It also evaluates trust between a user and members of the user's network to estimates if these members are acquaintances or adversaries based on the paths of the information flow between them. Finally, it provides more precise and viable information sharing decisions and enables better privacy control in the social network. We have evaluated our model with extensive experiments using both synthetic and real users' networks to demonstrate its ability to provide a naïve user with a good means of privacy protection. We have validated separately every phase of the model and examined the decisions obtained by two different approaches. The results show a strong correlation between the decisions made by the algorithm and the users' decisions.

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基于信任的在线社交网络隐私提供模型
在线社交网络(OSN)已经成为世界各地人们交流和互动的主要手段。在过去的二十年里,随着技术的进步,在网络社区中分享内容的活跃成员获得了利益和社会可见性,隐私的本质受到了挑战。当OSN用户与朋友和同事分享个人内容时,他们并不总是完全意识到他们的信息可能会在无意中暴露给各种人,包括攻击者、社交机器人、假用户、垃圾邮件发送者或数据收集者。防止这些信息泄露是为osn开发的许多安全模型的关键目标,包括访问控制、基于关系的模型、基于信任的模型和信息流控制。根据以往的研究,我们认为需要一种组合的方法来克服每个模型的缺点。在本文中,我们提出了一个新的用户隐私保护模型,该模型由三个主要阶段组成,涉及三个主要方面:信任、基于角色的访问控制和信息流。该模型考虑用户的子网,并对用户与角色的直连进行分类。它依靠诸如好友总数、用户帐户年龄和友谊持续时间等公共信息来表征网络连接的质量。它还评估用户和用户网络成员之间的信任,以根据他们之间信息流的路径估计这些成员是熟人还是对手。最后,它提供了更精确和可行的信息共享决策,并使社交网络中的隐私控制更好。我们通过使用合成和真实用户网络的大量实验来评估我们的模型,以证明它能够为naïve用户提供良好的隐私保护手段。我们分别验证了模型的每个阶段,并检查了通过两种不同方法获得的决策。结果表明,算法做出的决策与用户的决策之间存在很强的相关性。
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来源期刊
Online Social Networks and Media
Online Social Networks and Media Social Sciences-Communication
CiteScore
10.60
自引率
0.00%
发文量
32
审稿时长
44 days
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