PriMe: A Novel Privacy Measuring Framework for Online Social Networks

Ahmad Hassanpour, Bian Yang
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Abstract

Online Social Networks are responsible for disclosing a large amount of sensitive information. Users unintentionally reveal their sensitive information and are unaware of the privacy risks involved. But the users should be well informed about their privacy quotient and should know where they stand on the privacy measuring scale. In this paper, we proposed an adaptive privacy measuring framework called PriMe that can measure the privacy leakage score for each action of a user in an OSN and subsequently adjust the privacy settings based on the preferred privacy scopes and boundaries. Various types of data, actions, and personal characteristics of each user have been considered to ensure the calculated privacy leakage score is accurate.
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一种新的在线社交网络隐私测量框架
在线社交网络负责泄露大量敏感信息。用户无意中泄露了他们的敏感信息,并没有意识到所涉及的隐私风险。但用户应该充分了解他们的隐私商,应该知道他们在隐私测量量表上的位置。在本文中,我们提出了一种自适应隐私测量框架PriMe,它可以测量用户在OSN中的每个动作的隐私泄露得分,然后根据首选隐私范围和边界调整隐私设置。考虑了每个用户的各种类型的数据、行为和个人特征,以确保计算出的隐私泄露评分的准确性。
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