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首页> 外文期刊>International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems >A METRIC TO EVALUATE INTERACTION OBFUSCATION IN ONLINE SOCIAL NETWORKS
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A METRIC TO EVALUATE INTERACTION OBFUSCATION IN ONLINE SOCIAL NETWORKS

机译：在线社交网络中评估交互混淆度的度量

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摘要

Online social networks (OSNs) have become one of the main communication channels in today's information society, and their emergence has raised new privacy concerns. The content uploaded to OSNs (such as pictures, status updates, comments) is by default available to the OSN provider, and often to other people to whom the user who uploaded the content did not intend to give access. A different class of concerns relates to sensitive information that can be inferred from the behavior of users. For example, the analysis of user interactions augments social network graphs with potentially privacy-sensitive details on the nature of social relations, such as the strength of user relationships. A solution to prevent such inferences is to automatically generate dummy interactions that obfuscate the real interactions between OSN users. Given an adversary that observes the obfuscated interactions, the goal is to prevent the adversary from recovering parameters of interest (e.g., relationships strength) that accurately describe the real user interactions. The design and evaluation of obfuscation strategies requires metrics that express the level of protection they would offer when deployed in a particular OSN with its underlying user interaction patterns. In this paper we propose mutual information as obfuscation metric. It measures the amount of information leaked by the (observable) obfuscated interactions in the system on the (concealed) real interactions between users. We show that the metric is suitable for comparing different obfuscation strategies, and flexible to accommodate different network topologies and user communication patterns. Obfuscation comes at the cost of network overhead, and the proposed metric contributes to enabling the optimization of strategies to achieve good levels of privacy protection at minimum overhead. We provide a detailed methodology to compute the metric and perform experiments that illustrate its suitability.
机译：在线社交网络（OSN）已成为当今信息社会中的主要通信渠道之一，它们的出现引起了新的隐私问题。默认情况下，上载到OSN的内容（例如图片，状态更新，评论）可用于OSN提供程序，并且通常可用于其他上载了内容的用户不希望访问的人。不同类型的关注点涉及可以从用户的行为推断出的敏感信息。例如，对用户交互的分析会在社交关系的本质（例如用户关系的强度）上使用潜在的隐私敏感细节来增强社交网络图。防止此类推断的解决方案是自动生成虚拟交互，使OSN用户之间的真实交互变得模糊。给定观察到混淆的交互的对手，目标是防止该对手恢复准确地描述真实用户交互的感兴趣的参数（例如，关系强度）。混淆策略的设计和评估需要度量标准，这些度量标准表示它们在具有特定的用户交互模式的特定OSN中部署时将提供的保护级别。在本文中，我们提出互信息作为混淆度量。它根据用户之间的（隐藏的）真实交互来衡量系统中（可见的）混淆交互所泄漏的信息量。我们表明，该度量标准适用于比较不同的混淆策略，并且可以灵活地适应不同的网络拓扑和用户通信模式。混淆是以网络开销为代价的，并且所提出的度量标准有助于实现策略的优化，从而以最小的开销实现良好的隐私保护级别。我们提供了一种详细的方法来计算该指标并执行说明其适用性的实验。

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