连续时间扩散网络中隐私与效用的调和

M. Backes, M. Gomez-Rodriguez, Praveen Manoharan, Bartlomiej Surma
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引用次数: 3

摘要

社交网络和其他社交媒体系统是一种非常流行的媒介,它允许用户与他们的同伴自由交流和互动。然而,一旦用户共享了一条信息,信息在此类系统中的传递传播可以使该信息在整个系统中迅速传播。由于共享信息的潜在敏感性,用户自然希望控制信息的传播以确保隐私。同时,对于想要与之共享某条信息的用户,用户也有实用需求,这自然会与隐私需求产生冲突。在本文中,我们解决了通过社交网络控制信息传播的问题,同时保持用户设定的实用需求。我们利用连续时间扩散网络来模拟社交网络中的全球传播行为,并定义了隐私和效用组合策略,使我们能够在效用限制下强制执行隐私,反之亦然。我们表明,最优地满足这些策略对应于解决一个受限的次模最小化问题,该问题虽然np困难,但由于目标函数的结构,允许常数因子近似。
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Reconciling Privacy and Utility in Continuous-Time Diffusion Networks
Social Networks and other social media systems are an ever popular medium that allow users to freely communicate and interact with their peers. Once a user shares a piece of information, however, the transitive propagation of information in such systems can allow this information to spread quickly throughout the whole system. Due to the potentially sensitive nature of the shared information, users naturally have an interest in controlling the propagation of information to ensure privacy. At the same time, users also have utility requirements in terms of users they want to share a certain piece of information with, which naturally causes a conflict with the privacy requirements.,,In this paper, we tackle the issue of controlling the propagation of information through a social network while at the same time maintaining utility requirements set by the user. We leverage continuous-time diffusion networks to model the global propagation behavior in social networks and define combined privacy and utility policies that allow us to enforce privacy under utility restrictions, and vice versa. We show that optimally satisfying such policies corresponds to solving a constrained submodular minimization problem, which, while NP-hard, allows for a constant factor approximation due to the structure of our objective function.
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