Access-Control Lists (ACLs) (a.k.a. friend lists) are one of the most important privacy features of Online Social Networks (OSNs) as they allow users to restrict the audience of their publications. Nevertheless, creating and maintaining custom ACLs can introduce a high cognitive burden on average OSNs users since it normally requires assessing the trustworthiness of a large number of contacts. In principle, community detection algorithms can be leveraged to support the generation of ACLs by mapping a set of examples (i.e. contacts labelled as untrusted) to the emerging communities inside the user's ego-network. However, unlike users' access-control preferences, traditional community-detection algorithms do not take the homophily characteristics of such communities into account (i.e. attributes shared among members). Consequently, this strategy may lead to inaccurate ACL configurations and privacy breaches under certain homophily scenarios. This work investigates the use of community-detection algorithms for the automatic generation of ACLs in OSNs. Particularly, it analyses the performance of the aforementioned approach under different homophily conditions through a simulation model. Furthermore, since private information may reach the scope of untrusted recipients through the re-sharing affordances of OSNs, information diffusion processes are also modelled and taken explicitly into account. Altogether, the removal of gatekeeper nodes is further explored as a strategy to counteract unwanted data dissemination.

翻译：访问控制列表(ACL)(a.k.a.a.friend list)是在线社会网络(OSNs)最重要的隐私特征之一,因为在线社会网络允许用户限制其出版物的读者,然而,创建和维持定制访问控制列表可对普通的OSNS用户带来很高的认知负担,因为通常需要评估大量接触的可信度。原则上,社区检测算法可以用来支持创建访问控制列表,方法是向用户的自我网络内新兴社区绘制一系列例子(即被称为不受信任的联系人),然而,与用户的访问控制偏好不同,传统的社区检测算法并不考虑这些社区的同质特征(即成员之间共享的属性),因此,这一战略可能会在某些同质假设情景下造成不准确的ACL配置和侵犯隐私的情况。 这项工作调查了社区检测算法用于在OSNs自动生成ACLs的情况。 特别是,它分析上述方法在不同的同性访问控制选项下的表现,传统的社区检测算法没有考虑到这些社区的同源端战略的传播方式,因此,通过模拟性信任模式的传播模式的私人信息范围也无法进入。